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Finding the Global Semantic Representation in GAN through Fréchet Mean
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https://openreview.net/forum?id=9ImtNIZ7bYx
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https://openreview.net/forum?id=9ImtNIZ7bYx
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Jaewoong Choi,Geonho Hwang,Hyunsoo Cho,Myungjoo Kang
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ICLR 2023,Poster
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The ideally disentangled latent space in GAN involves the global representation of latent space using semantic attribute coordinates. In other words, in this disentangled space, there exists the global semantic basis as a vector space where each basis component describes one attribute of generated images. In this paper, we propose an unsupervised method for finding this global semantic basis in the intermediate latent space in GANs. This semantic basis represents sample-independent meaningful perturbations that change the same semantic attribute of an image on the entire latent space. The proposed global basis, called Fréchet basis, is derived by introducing Fréchet mean to the local semantic perturbations in a latent space. Fréchet basis is discovered in two stages. First, the global semantic subspace is discovered by the Fréchet mean in the Grassmannian manifold of the local semantic subspaces. Second, Fréchet basis is found by optimizing a basis of the semantic subspace via the Fréchet mean in the Special Orthogonal Group. Experimental results demonstrate that Fréchet basis provides better semantic factorization and robustness compared to the previous methods. Moreover, we suggest the basis refinement scheme for the previous methods. The quantitative experiments show that the refined basis achieves better semantic factorization while constrained on the same semantic subspace given by the previous method.
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https://openreview.net/pdf/bbc55d277c91f0050961e6a5e0937b7380a176d7.pdf
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MultiViz: Towards Visualizing and Understanding Multimodal Models
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https://openreview.net/forum?id=i2_TvOFmEml
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https://openreview.net/forum?id=i2_TvOFmEml
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Paul Pu Liang,Yiwei Lyu,Gunjan Chhablani,Nihal Jain,Zihao Deng,Xingbo Wang,Louis-Philippe Morency,Ruslan Salakhutdinov
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ICLR 2023,Poster
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The promise of multimodal models for real-world applications has inspired research in visualizing and understanding their internal mechanics with the end goal of empowering stakeholders to visualize model behavior, perform model debugging, and promote trust in machine learning models. However, modern multimodal models are typically black-box neural networks, which makes it challenging to understand their internal mechanics. How can we visualize the internal modeling of multimodal interactions in these models? Our paper aims to fill this gap by proposing MultiViz, a method for analyzing the behavior of multimodal models by scaffolding the problem of interpretability into 4 stages: (1) unimodal importance: how each modality contributes towards downstream modeling and prediction, (2) cross-modal interactions: how different modalities relate with each other, (3) multimodal representations: how unimodal and cross-modal interactions are represented in decision-level features, and (4) multimodal prediction: how decision-level features are composed to make a prediction. MultiViz is designed to operate on diverse modalities, models, tasks, and research areas. Through experiments on 8 trained models across 6 real-world tasks, we show that the complementary stages in MultiViz together enable users to (1) simulate model predictions, (2) assign interpretable concepts to features, (3) perform error analysis on model misclassifications, and (4) use insights from error analysis to debug models. MultiViz is publicly available, will be regularly updated with new interpretation tools and metrics, and welcomes inputs from the community.
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https://openreview.net/pdf/34c8d3975874963fb014c66baab9fc0b36443d92.pdf
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How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression?
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https://openreview.net/forum?id=sKHqgFOaFXI
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https://openreview.net/forum?id=sKHqgFOaFXI
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Jetze Schuurmans,kim batselier,Julian Kooij
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ICLR 2023,Poster
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Tensor decompositions have been successfully applied to compress neural networks. The compression algorithms using tensor decompositions commonly minimize the approximation error on the weights. Recent work assumes the approximation error on the weights is a proxy for the performance of the model to compress multiple layers and fine-tune the compressed model. Surprisingly, little research has systematically evaluated which approximation errors can be used to make choices regarding the layer, tensor decomposition method, and level of compression. To close this gap, we perform an experimental study to test if this assumption holds across different layers and types of decompositions, and what the effect of fine-tuning is. We include the approximation error on the features resulting from a compressed layer in our analysis to test if this provides a better proxy, as it explicitly takes the data into account. We find the approximation error on the weights has a positive correlation with the performance error, before as well as after fine-tuning. Basing the approximation error on the features does not improve the correlation significantly. While scaling the approximation error commonly is used to account for the different sizes of layers, the average correlation across layers is smaller than across all choices (i.e. layers, decompositions, and level of compression) before fine-tuning. When calculating the correlation across the different decompositions, the average rank correlation is larger than across all choices. This means multiple decompositions can be considered for compression and the approximation error can be used to choose between them.
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https://openreview.net/pdf/db4bcb36915a563034859380e0e23f3fbe678d4e.pdf
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Blurring Diffusion Models
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https://openreview.net/forum?id=OjDkC57x5sz
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https://openreview.net/forum?id=OjDkC57x5sz
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Emiel Hoogeboom,Tim Salimans
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ICLR 2023,Poster
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Recently, Rissanen et al., (2022) have presented a new type of diffusion process for generative modeling based on heat dissipation, or blurring, as an alternative to isotropic Gaussian diffusion. Here, we show that blurring can equivalently be defined through a Gaussian diffusion process with non-isotropic noise. In making this connection, we bridge the gap between inverse heat dissipation and denoising diffusion, and we shed light on the inductive bias that results from this modeling choice. Finally, we propose a generalized class of diffusion models that offers the best of both standard Gaussian denoising diffusion and inverse heat dissipation, which we call Blurring Diffusion Models.
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https://openreview.net/pdf/33c05b0bd300e12f24e2ac7a85f4716f5c4a9147.pdf
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Hyperbolic Self-paced Learning for Self-supervised Skeleton-based Action Representations
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https://openreview.net/forum?id=3Bh6sRPKS3J
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https://openreview.net/forum?id=3Bh6sRPKS3J
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Luca Franco,Paolo Mandica,Bharti Munjal,Fabio Galasso
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ICLR 2023,Poster
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Self-paced learning has been beneficial for tasks where some initial knowledge is available, such as weakly supervised learning and domain adaptation, to select and order the training sample sequence, from easy to complex. However its applicability remains unexplored in unsupervised learning, whereby the knowledge of the task matures during training.
We propose a novel HYperbolic Self-Paced model (HYSP) for learning skeletonbased action representations. HYSP adopts self-supervision: it uses data augmentations to generate two views of the same sample, and it learns by matching one (named online) to the other (the target). We propose to use hyperbolic uncertainty to determine the algorithmic learning pace, under the assumption that less uncertain samples should be more strongly driving the training, with a larger weight and pace. Hyperbolic uncertainty is a by-product of the adopted hyperbolic neural networks, it matures during training and it comes with no extra cost, compared to the established Euclidean SSL framework counterparts.
When tested on three established skeleton-based action recognition datasets, HYSP outperforms the state-of-the-art on PKU-MMD I, as well as on 2 out of 3 downstream tasks on NTU-60 and NTU-120. Additionally, HYSP only uses positive pairs and bypasses therefore the complex and computationally-demanding mining procedures required for the negatives in contrastive techniques.
Code is available at https://github.com/paolomandica/HYSP.
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https://openreview.net/pdf/e9d939f7209fc025471eb75f6f726e179fc88509.pdf
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Efficient Offline Policy Optimization with a Learned Model
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https://openreview.net/forum?id=Yt-yM-JbYFO
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https://openreview.net/forum?id=Yt-yM-JbYFO
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Zichen Liu,Siyi Li,Wee Sun Lee,Shuicheng YAN,Zhongwen Xu
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ICLR 2023,Poster
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MuZero Unplugged presents a promising approach for offline policy learning from logged data. It conducts Monte-Carlo Tree Search (MCTS) with a learned model and leverages Reanalyze algorithm to learn purely from offline data. For good performance, MCTS requires accurate learned models and a large number of simulations, thus costing huge computing time. This paper investigates a few hypotheses where MuZero Unplugged may not work well under the offline RL settings, including 1) learning with limited data coverage; 2) learning from offline data of stochastic environments; 3) improperly parameterized models given the offline data; 4) with a low compute budget. We propose to use a regularized one-step look-ahead approach to tackle the above issues. Instead of planning with the expensive MCTS, we use the learned model to construct an advantage estimation based on a one-step rollout. Policy improvements are towards the direction that maximizes the estimated advantage with regularization of the dataset. We conduct extensive empirical studies with BSuite environments to verify the hypotheses and then run our algorithm on the RL Unplugged Atari benchmark. Experimental results show that our proposed approach achieves stable performance even with an inaccurate learned model. On the large-scale Atari benchmark, the proposed method outperforms MuZero Unplugged by 43%. Most significantly, it uses only 5.6% wall-clock time (i.e., 1 hour) compared to MuZero Unplugged (i.e., 17.8 hours) to achieve a 150% IQM normalized score with the same hardware and software stacks. Our implementation is open-sourced at https://github.com/sail-sg/rosmo.
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https://openreview.net/pdf/a81d03d3b79d745279c3247be0d1b9b248e37300.pdf
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New Insights for the Stability-Plasticity Dilemma in Online Continual Learning
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https://openreview.net/forum?id=fxC7kJYwA_a
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https://openreview.net/forum?id=fxC7kJYwA_a
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Dahuin Jung,Dongjin Lee,Sunwon Hong,Hyemi Jang,Ho Bae,Sungroh Yoon
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ICLR 2023,Poster
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The aim of continual learning is to learn new tasks continuously (i.e., plasticity) without forgetting previously learned knowledge from old tasks (i.e., stability). In the scenario of online continual learning, wherein data comes strictly in a streaming manner, the plasticity of online continual learning is more vulnerable than offline continual learning because the training signal that can be obtained from a single data point is limited. To overcome the stability-plasticity dilemma in online continual learning, we propose an online continual learning framework named multi-scale feature adaptation network (MuFAN) that utilizes a richer context encoding extracted from different levels of a pre-trained network. Additionally, we introduce a novel structure-wise distillation loss and replace the commonly used batch normalization layer with a newly proposed stability-plasticity normalization module to train MuFAN that simultaneously maintains high plasticity and stability. MuFAN outperforms other state-of-the-art continual learning methods on the SVHN, CIFAR100, miniImageNet, and CORe50 datasets. Extensive experiments and ablation studies validate the significance and scalability of each proposed component: 1) multi-scale feature maps from a pre-trained encoder, 2) the structure-wise distillation loss, and 3) the stability-plasticity normalization module in MuFAN. Code is publicly available at https://github.com/whitesnowdrop/MuFAN.
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https://openreview.net/pdf/3b6a297e3a6c2f568ec5b521b172a207403e9f93.pdf
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MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer
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https://openreview.net/forum?id=dRjWsd3gwsm
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https://openreview.net/forum?id=dRjWsd3gwsm
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Qihao Zhao,Yangyu Huang,Wei Hu,Fan Zhang,Jun Liu
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ICLR 2023,Poster
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The recently proposed data augmentation TransMix employs attention labels to help visual transformers (ViT) achieve better robustness and performance. However, TransMix is deficient in two aspects: 1) The image cropping method of TransMix may not be suitable for vision transformer. 2) At the early stage of training, the model produces unreliable attention maps. TransMix uses unreliable attention maps to compute mixed attention labels that can affect the model. To address the aforementioned issues, we propose MaskMix and Progressive Attention Labeling (PAL) in image and label space, respectively. In detail, from the perspective of image space, we design MaskMix, which mixes two images based on a patch-like grid mask. In particular, the size of each mask patch is adjustable and is a multiple of the image patch size, which ensures each image patch comes from only one image and contains more global contents. From the perspective of label space, we design PAL, which utilizes a progressive factor to dynamically re-weight the attention weights of the mixed attention label. Finally, we combine MaskMix and Progressive Attention Labeling as our new data augmentation method, named MixPro. The experimental results show that our method can improve various ViT-based models at scales on ImageNet classification (73.8% top-1 accuracy based on DeiT-T for 300 epochs). After being pre-trained with MixPro on ImageNet, the ViT-based models also demonstrate better transferability to semantic segmentation, object detection, and instance segmentation. Furthermore, compared to TransMix, MixPro also shows stronger robustness on several benchmarks.
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https://openreview.net/pdf/0958c1042f7b6e9c288b5bec82ced0c146f4bf51.pdf
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StyleMorph: Disentangled 3D-Aware Image Synthesis with a 3D Morphable StyleGAN
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https://openreview.net/forum?id=Ojpb1y8jflw
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https://openreview.net/forum?id=Ojpb1y8jflw
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Eric-Tuan Le,Edward Bartrum,Iasonas Kokkinos
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ICLR 2023,Poster
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We introduce StyleMorph, a 3D-aware generative model that disentangles 3D shape, camera pose, object appearance, and background appearance for high quality image synthesis. We account for shape variability by morphing a canonical 3D object template, effectively learning a 3D morphable model in an entirely unsupervised manner through backprop. We chain 3D morphable modelling with deferred neural rendering by performing an implicit surface rendering of “Template Object Coordinates” (TOCS), which can be understood as an unsupervised counterpart to UV maps. This provides a detailed 2D TOCS map signal that reflects the compounded geometric effects of non-rigid shape variation, camera pose, and perspective projection. We combine 2D TOCS maps with an independent appearance code to condition a StyleGAN-based deferred neural rendering (DNR) network for foreground image (object) synthesis; we use a separate code for background synthesis and do late fusion to deliver the final result. We show competitive synthesis results on 4 datasets (FFHQ faces, AFHQ Cats, Dogs, Wild), while achieving the joint disentanglement of shape, pose, object and background texture.
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https://openreview.net/pdf/6c65dce0c014a5dabd0d9668059d99ef3bf40a95.pdf
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Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective
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https://openreview.net/forum?id=Dvs-a3aymPe
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https://openreview.net/forum?id=Dvs-a3aymPe
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Kun Wang,Yuxuan Liang,Pengkun Wang,Xu Wang,Pengfei Gu,Junfeng Fang,Yang Wang
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ICLR 2023,Poster
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Graph Neural Networks (GNNs) have shown great promise in various graph learning tasks. However, the computational overheads of fitting GNNs to large-scale graphs grow rapidly, posing obstacles to GNNs from scaling up to real-world applications. To tackle this issue, Graph Lottery Ticket (GLT) hypothesis articulates that there always exists a sparse subnetwork/subgraph with admirable performance in GNNs with random initialization. Such a pair of core subgraph and sparse subnetwork (called graph lottery tickets) can be uncovered by iteratively applying a novel sparsification method. While GLT provides new insights for GNN compression, it requires a full pretraining process to obtain graph lottery tickets, which is not universal and friendly to real-world applications. Moreover, the graph sparsification in GLT utilizes sampling techniques, which may result in massive information loss and aggregation failure. In this paper, we explore the searching of graph lottery tickets from a complementary perspective -- transforming a random ticket into a graph lottery ticket, which allows us to more comprehensively explore the relationships between the original network/graph and their sparse counterpart. To achieve this, we propose regularization-based network pruning and hierarchical graph sparsification, leading to our Dual Graph Lottery Ticket (DGLT) framework for a joint sparsification of network and graph. Compared to GLT, our DGLT helps achieve a triple-win situation of graph lottery tickets with high sparsity, admirable performance, and good explainability. More importantly, we rigorously prove that our model can eliminate noise and maintain reliable information in substructures using the graph information bottleneck theory. Extensive experimental results on various graph-related tasks validate the effectiveness of our framework.
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https://openreview.net/pdf/82e765bfadf666ab82ee0cfe6bb7ea875f14a519.pdf
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Video Scene Graph Generation from Single-Frame Weak Supervision
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https://openreview.net/forum?id=KLrGlNoxzb4
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https://openreview.net/forum?id=KLrGlNoxzb4
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Siqi Chen,Jun Xiao,Long Chen
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ICLR 2023,Poster
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Video scene graph generation (VidSGG) aims to generate a sequence of graph-structure representations for the given video. However, all existing VidSGG methods are fully-supervised, i.e., they need dense and costly manual annotations. In this paper, we propose the first weakly-supervised VidSGG task with only single-frame weak supervision: SF-VidSGG. By ``weakly-supervised", we mean that SF-VidSGG relaxes the training supervision from two different levels: 1) It only provides single-frame annotations instead of all-frame annotations. 2) The single-frame ground-truth annotation is still a weak image SGG annotation, i.e., an unlocalized scene graph. To solve this new task, we also propose a novel Pseudo Label Assignment based method, dubbed as PLA. PLA is a two-stage method, which generates pseudo visual relation annotations for the given video at the first stage, and then trains a fully-supervised VidSGG model with these pseudo labels. Specifically, PLA consists of three modules: an object PLA module, a predicate PLA module, and a future predicate prediction (FPP) module. Firstly, in the object PLA, we localize all objects for every frame. Then, in the predicate PLA, we design two different teachers to assign pseudo predicate labels. Lastly, in the FPP module, we fusion these two predicate pseudo labels by the regularity of relation transition in videos. Extensive ablations and results on the benchmark Action Genome have demonstrated the effectiveness of our PLA.
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https://openreview.net/pdf/b5c5105754b28b58012086048717126a9148962a.pdf
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Unsupervised visualization of image datasets using contrastive learning
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https://openreview.net/forum?id=nI2HmVA0hvt
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https://openreview.net/forum?id=nI2HmVA0hvt
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Niklas Böhm,Philipp Berens,Dmitry Kobak
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ICLR 2023,Poster
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Visualization methods based on the nearest neighbor graph, such as t-SNE or UMAP, are widely used for visualizing high-dimensional data. Yet, these approaches only produce meaningful results if the nearest neighbors themselves are meaningful. For images represented in pixel space this is not the case, as distances in pixel space are often not capturing our sense of similarity and therefore neighbors are not semantically close. This problem can be circumvented by self-supervised approaches based on contrastive learning, such as SimCLR, relying on data augmentation to generate implicit neighbors, but these methods do not produce two-dimensional embeddings suitable for visualization. Here, we present a new method, called t-SimCNE, for unsupervised visualization of image data. T-SimCNE combines ideas from contrastive learning and neighbor embeddings, and trains a parametric mapping from the high-dimensional pixel space into two dimensions. We show that the resulting 2D embeddings achieve classification accuracy comparable to the state-of-the-art high-dimensional SimCLR representations, thus faithfully capturing semantic relationships. Using t-SimCNE, we obtain informative visualizations of the CIFAR-10 and CIFAR-100 datasets, showing rich cluster structure and highlighting artifacts and outliers.
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https://openreview.net/pdf/db7ad4f645b2457507e0e9aaa85e2ab8d0fc9cb1.pdf
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PowerQuant: Automorphism Search for Non-Uniform Quantization
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https://openreview.net/forum?id=s1KljJpAukm
|
https://openreview.net/forum?id=s1KljJpAukm
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Edouard YVINEC,Arnaud Dapogny,Matthieu Cord,Kevin Bailly
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ICLR 2023,Poster
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Deep neural networks (DNNs) are nowadays ubiquitous in many domains such as computer vision. However, due to their high latency, the deployment of DNNs hinges on the development of compression techniques such as quantization which consists in lowering the number of bits used to encode the weights and activations. Growing concerns for privacy and security have motivated the development of data-free techniques, at the expanse of accuracy. In this paper, we identity the uniformity of the quantization operator as a limitation of existing approaches, and propose a data-free non-uniform method. More specifically, we argue that to be readily usable without dedicated hardware and implementation, non-uniform quantization shall not change the nature of the mathematical operations performed by the DNN. This leads to search among the continuous automorphisms of $(\mathbb{R}_+^*,\times)$, which boils down to the power functions defined by their exponent. To find this parameter, we propose to optimize the reconstruction error of each layer: in particular, we show that this procedure is locally convex and admits a unique solution. At inference time, we show that our approach, dubbed PowerQuant, only require simple modifications in the quantized DNN activation functions. As such, with only negligible overhead, it significantly outperforms existing methods in a variety of configurations.
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https://openreview.net/pdf/128aab6307b8e0b4c0cc436f8a5b62d9ebe7565c.pdf
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BAYES RISK CTC: CONTROLLABLE CTC ALIGNMENT IN SEQUENCE-TO-SEQUENCE TASKS
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https://openreview.net/forum?id=Bd7GueaTxUz
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https://openreview.net/forum?id=Bd7GueaTxUz
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Jinchuan Tian,Brian Yan,Jianwei Yu,CHAO WENG,Dong Yu,Shinji Watanabe
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ICLR 2023,Poster
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Sequence-to-Sequence (seq2seq) tasks transcribe the input sequence to a target sequence. The Connectionist Temporal Classification (CTC) criterion is widely used in multiple seq2seq tasks. Besides predicting the target sequence, a side product of CTC is to predict the alignment, which is the most probable input-long sequence that specifies a hard aligning relationship between the input and target units. As there are multiple potential aligning sequences (called paths) that are equally considered in CTC formulation, the choice of which path will be most probable and become the predicted alignment is always uncertain. In addition, it is usually observed that the alignment predicted by vanilla CTC will drift compared with its reference and rarely provides practical functionalities. Thus, the motivation of this work is to make the CTC alignment prediction controllable and thus equip CTC with extra functionalities. The Bayes risk CTC (BRCTC) criterion is then proposed in this work, in which a customizable Bayes risk function is adopted to enforce the desired characteristics of the predicted alignment. With the risk function, the BRCTC is a general framework to adopt some customizable preference over the paths in order to concentrate the posterior into a particular subset of the paths. In applications, we explore one particular preference which yields models with the down-sampling ability and reduced inference costs. By using BRCTC with another preference for early emissions, we obtain an improved performance-latency trade-off for online models. Experimentally, the proposed BRCTC reduces the inference cost of offline models by up to 47% without performance degradation and cuts down the overall latency of online systems to an unseen level.
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https://openreview.net/pdf/48ab46d2930d93cc5f05acde4617f4db91dd4007.pdf
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A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
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https://openreview.net/forum?id=0jxPyVWmiiF
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https://openreview.net/forum?id=0jxPyVWmiiF
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Jiajin Li,Jianheng Tang,Lemin Kong,Huikang Liu,Jia Li,Anthony Man-Cho So,Jose Blanchet
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ICLR 2023,Poster
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In this work, we present the Bregman Alternating Projected Gradient (BAPG) method, a single-loop algorithm that offers an approximate solution to the Gromov-Wasserstein (GW) distance.
We introduce a novel relaxation technique that balances accuracy and computational efficiency, albeit with some compromises in the feasibility of the coupling map. Our analysis is based on the observation that the GW problem satisfies the Luo-Tseng error bound condition, which relates to estimating the distance of a point to the critical point set of the GW problem based on the optimality residual.
This observation allows us to provide an approximation bound for the distance between the fixed-point set of BAPG and the critical point set of GW. Moreover, under a mild technical assumption, we can show that BAPG converges to its fixed point set.
The effectiveness of BAPG has been validated through comprehensive numerical experiments in graph alignment and partition tasks, where it outperforms existing methods in terms of both solution quality and wall-clock time.
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https://openreview.net/pdf/f1fd07860b80238a145607a2cc1429011d414718.pdf
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Semi-supervised learning with a principled likelihood from a generative model of data curation
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https://openreview.net/forum?id=zOHQGKO3WGY
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https://openreview.net/forum?id=zOHQGKO3WGY
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Stoil Krasimirov Ganev,Laurence Aitchison
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ICLR 2023,Poster
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We currently do not have an understanding of semi-supervised learning (SSL) objectives such as pseudo-labelling and entropy minimization as log-likelihoods, which precludes the development of e.g. Bayesian SSL. Here, we note that benchmark image datasets such as CIFAR-10 are carefully curated, and we formulate SSL objectives as a log-likelihood in a generative model of data curation. We show that SSL objectives, from entropy minimization and pseudo-labelling, to state-of-the-art techniques similar to FixMatch can be understood as lower-bounds on our principled log-likelihood. We are thus able to introduce a Bayesian extension of SSL, which gives considerable improvements over standard SSL in the setting of 40 labelled points on CIFAR-10, with performance of $92.2\pm 0.3\%$ vs $88.6\%$ in the original FixMatch paper. Finally, our theory suggests that SSL is effective in part due to the statistical patterns induced by data curation. This provides an explanation of past results which show SSL performs better on clean datasets without any ``out of distribution'' examples. Confirming these results we find that SSL gave much larger performance improvements on curated than on uncurated data, using matched curated and uncurated datasets based on Galaxy Zoo 2.
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https://openreview.net/pdf/8ef08a13d823e8db4ab1c9d60fedf5e49c81c66b.pdf
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E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
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https://openreview.net/forum?id=sO1QiAftQFv
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https://openreview.net/forum?id=sO1QiAftQFv
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Yangtian Zhang,Huiyu Cai,Chence Shi,Jian Tang
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ICLR 2023,Poster
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In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery.
This work focuses on blind flexible self-docking, where we aim to predict the positions, orientations and conformations of docked molecules. Traditional physics-based methods usually suffer from inaccurate scoring functions and high inference cost. Recently, data-driven methods based on deep learning techniques are attracting growing interest thanks to their efficiency during inference and promising performance. These methods usually either adopt a two-stage approach by first predicting the distances between proteins and ligands and then generating the final coordinates based on the predicted distances, or directly predicting the global roto-translation of ligands. In this paper, we take a different route. Inspired by the resounding success of AlphaFold2 for protein structure prediction, we propose E3Bind, an end-to-end equivariant network that iteratively updates the ligand pose. E3Bind models the protein-ligand interaction through careful consideration of the geometric constraints in docking and the local context of the binding site. Experiments on standard benchmark datasets demonstrate the superior performance of our end-to-end trainable model compared to traditional and recently-proposed deep learning methods.
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https://openreview.net/pdf/316cc340d0f6c5ff0eff49c211af90a0b26d6896.pdf
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Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization
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https://openreview.net/forum?id=Q-WfHzmiG9m
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https://openreview.net/forum?id=Q-WfHzmiG9m
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Sangwon Jung,Taeeon Park,Sanghyuk Chun,Taesup Moon
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ICLR 2023,Poster
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Many existing group fairness-aware training methods aim to achieve the group fairness by either re-weighting underrepresented groups based on certain rules or using weakly approximated surrogates for the fairness metrics in the objective as regularization terms. Although each of the learning schemes has its own strength in terms of applicability or performance, respectively, it is difficult for any method in the either category to be considered as a gold standard since their successful performances are typically limited to specific cases. To that end, we propose a principled method, dubbed as FairDRO, which unifies the two learning schemes by incorporating a well-justified group fairness metric into the training objective using a classwise distributionally robust optimization (DRO) framework. We then develop an iterative optimization algorithm that minimizes the resulting objective by automatically producing the correct re-weights for each group. Our experiments show that FairDRO is scalable and easily adaptable to diverse applications, and consistently achieves the state-of-the-art performance on several benchmark datasets in terms of the accuracy-fairness trade-off, compared to recent strong baselines.
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https://openreview.net/pdf/3d04ed35e723d0cb19c844f0eac6dc274eee5fcf.pdf
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Are More Layers Beneficial to Graph Transformers?
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https://openreview.net/forum?id=uagC-X9XMi8
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https://openreview.net/forum?id=uagC-X9XMi8
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Haiteng Zhao,Shuming Ma,Dongdong Zhang,Zhi-Hong Deng,Furu Wei
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ICLR 2023,Poster
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Despite that going deep has proven successful in many neural architectures, the existing graph transformers are relatively shallow. In this work, we explore whether more layers are beneficial to graph transformers, and find that current graph transformers suffer from the bottleneck of improving performance by increasing depth. Our further analysis reveals the reason is that deep graph transformers are limited by the vanishing capacity of global attention, restricting the graph transformer from focusing on the critical substructure and obtaining expressive features. To this end, we propose a novel graph transformer model named DeepGraph that explicitly employs substructure tokens in the encoded representation, and applies local attention on related nodes to obtain substructure based attention encoding. Our model enhances the ability of the global attention to focus on substructures and promotes the expressiveness of the representations, addressing the limitation of self-attention as the graph transformer deepens. Experiments show that our method unblocks the depth limitation of graph transformers and results in state-of-the-art performance across various graph benchmarks with deeper models.
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https://openreview.net/pdf/8cf69f5b014d1ccaf06a2e7a81005d1a5b08d259.pdf
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Simplicial Hopfield networks
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https://openreview.net/forum?id=_QLsH8gatwx
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https://openreview.net/forum?id=_QLsH8gatwx
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Thomas F Burns,Tomoki Fukai
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ICLR 2023,Poster
|
Hopfield networks are artificial neural networks which store memory patterns on the states of their neurons by choosing recurrent connection weights and update rules such that the energy landscape of the network forms attractors around the memories. How many stable, sufficiently-attracting memory patterns can we store in such a network using $N$ neurons? The answer depends on the choice of weights and update rule. Inspired by setwise connectivity in biology, we extend Hopfield networks by adding setwise connections and embedding these connections in a simplicial complex. Simplicial complexes are higher dimensional analogues of graphs which naturally represent collections of pairwise and setwise relationships. We show that our simplicial Hopfield networks increase memory storage capacity. Surprisingly, even when connections are limited to a small random subset of equivalent size to an all-pairwise network, our networks still outperform their pairwise counterparts. Such scenarios include non-trivial simplicial topology. We also test analogous modern continuous Hopfield networks, offering a potentially promising avenue for improving the attention mechanism in Transformer models.
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https://openreview.net/pdf/e23c85adef751869d6c53a9e15879e8940567192.pdf
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Versatile Neural Processes for Learning Implicit Neural Representations
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https://openreview.net/forum?id=2nLeOOfAjK
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https://openreview.net/forum?id=2nLeOOfAjK
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Zongyu Guo,Cuiling Lan,Zhizheng Zhang,Yan Lu,Zhibo Chen
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ICLR 2023,Poster
|
Representing a signal as a continuous function parameterized by neural network (a.k.a. Implicit Neural Representations, INRs) has attracted increasing attention in recent years. Neural Processes (NPs), which model the distributions over functions conditioned on partial observations (context set), provide a practical solution for fast inference of continuous functions. However, existing NP architectures suffer from inferior modeling capability for complex signals. In this paper, we propose an efficient NP framework dubbed Versatile Neural Processes (VNP), which largely increases the capability of approximating functions. Specifically, we introduce a bottleneck encoder that produces fewer and informative context tokens, relieving the high computational cost while providing high modeling capability. At the decoder side, we hierarchically learn multiple global latent variables that jointly model the global structure and the uncertainty of a function, enabling our model to capture the distribution of complex signals. We demonstrate the effectiveness of the proposed VNP on a variety of tasks involving 1D, 2D and 3D signals. Particularly, our method shows promise in learning accurate INRs w.r.t. a 3D scene without further finetuning.
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https://openreview.net/pdf/0cf501061113960d14dd8752084564e00943445a.pdf
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Classically Approximating Variational Quantum Machine Learning with Random Fourier Features
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https://openreview.net/forum?id=ymFhZxw70uz
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https://openreview.net/forum?id=ymFhZxw70uz
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Jonas Landman,Slimane Thabet,Constantin Dalyac,Hela Mhiri,Elham Kashefi
|
ICLR 2023,Poster
|
Many applications of quantum computing in the near term rely on variational quantum circuits (VQCs). They have been showcased as a promising model for reaching a quantum advantage in machine learning with current noisy intermediate scale quantum computers (NISQ). It is often believed that the power of VQCs relies on their exponentially large feature space, and extensive works have explored the expressiveness and trainability of VQCs in that regard. In our work, we propose a classical sampling method that can closely approximate most VQCs with Hamiltonian encoding, given only the description of their architecture. It uses the seminal proposal of Random Fourier Features (RFF) and the fact that VQCs can be seen as large Fourier series. We show theoretically and experimentally that models built from exponentially large quantum feature space can be classically reproduced by sampling a few frequencies to build an equivalent low dimensional kernel. Precisely, we show that the number of required samples grows favourably with the size of the quantum spectrum. This tool therefore questions the hope for quantum advantage from VQCs in many cases, but conversely helps to narrow the conditions for their potential success. We expect VQCs with various and complex encoding Hamiltonians, or with large input dimension, to become more robust to classical approximations.
|
https://openreview.net/pdf/003659588365cb4189ee6ae152e6c751e108d97e.pdf
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Distributional Meta-Gradient Reinforcement Learning
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https://openreview.net/forum?id=LGkmUauBUL
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https://openreview.net/forum?id=LGkmUauBUL
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Haiyan Yin,Shuicheng YAN,Zhongwen Xu
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ICLR 2023,Poster
|
Meta-gradient reinforcement learning (RL) algorithms have substantially boosted the performance of RL agents by learning an adaptive return. All the existing algorithms adhere to the same reward learning principle, where the adaptive return is simply formulated in the form of expected cumulative rewards, upon which the policy and critic update rules are specified under well-adopted distance metrics. In this paper, we present a novel algorithm that builds on the success of meta-gradient RL algorithms and effectively improves such algorithms by following a simple recipe, i.e., going beyond the expected return to formulate and learn the return in a more expressive form, value distributions. To this end, we first formulate a distributional return that could effectively capture bootstrapping and discounting behaviors over distributions, to form an informative distributional return target in value update. Then we derive an efficient meta update rule to learn the adaptive distributional return with meta-gradients. For empirical evaluation, we first present an illustrative example on a toy two-color grid-world domain, which validates the benefit of learning distributional return over expectation; then we conduct extensive comparisons on a large-scale RL benchmark Atari 2600, where we confirm that our proposed method with distributional return works seamlessly well with the actor-critic framework and leads to state-of-the-art median human normalized score among meta-gradient RL literature.
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https://openreview.net/pdf/fbd330b31dd69cf244b53291887b18ab9c2989b8.pdf
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A Differential Geometric View and Explainability of GNN on Evolving Graphs
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https://openreview.net/forum?id=lRdhvzMpVYV
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https://openreview.net/forum?id=lRdhvzMpVYV
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Yazheng Liu,Xi Zhang,Sihong Xie
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ICLR 2023,Poster
|
Graphs are ubiquitous in social networks and biochemistry, where Graph Neural Networks (GNN) are the state-of-the-art models for prediction. Graphs can be evolving and it is vital to formally model and understand how a trained GNN responds to graph evolution. We propose a smooth parameterization of the GNN predicted distributions using axiomatic attribution, where the distributions are on a low dimensional manifold within a high-dimensional embedding space. We exploit the differential geometric viewpoint to model distributional evolution as smooth curves on the manifold. We reparameterize families of curves on the manifold and design a convex optimization problem to find a unique curve that concisely approximates the distributional evolution for human interpretation. Extensive experiments on node classification, link prediction, and graph classification tasks with evolving graphs demonstrate the better sparsity, faithfulness, and intuitiveness of the proposed method over the state-of-the-art methods.
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https://openreview.net/pdf/a3918635757574d63bba806cbd417f5a14b58811.pdf
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$\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks
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https://openreview.net/forum?id=7L2mgi0TNEP
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https://openreview.net/forum?id=7L2mgi0TNEP
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Zeyu Zhu,Fanrong Li,Zitao Mo,Qinghao Hu,Gang Li,Zejian Liu,Xiaoyao Liang,Jian Cheng
|
ICLR 2023,Poster
|
As graph data size increases, the vast latency and memory consumption during inference pose a significant challenge to the real-world deployment of Graph Neural Networks (GNNs). While quantization is a powerful approach to reducing GNNs complexity, most previous works on GNNs quantization fail to exploit the unique characteristics of GNNs, suffering from severe accuracy degradation. Through an in-depth analysis of the topology of GNNs, we observe that the topology of the graph leads to significant differences between nodes, and most of the nodes in a graph appear to have a small aggregation value. Motivated by this, in this paper, we propose the Aggregation-Aware mixed-precision Quantization ($\rm A^2Q$) for GNNs, where an appropriate bitwidth is automatically learned and assigned to each node in the graph. To mitigate the vanishing gradient problem caused by sparse connections between nodes, we propose a Local Gradient method to serve the quantization error of the node features as the supervision during training. We also develop a Nearest Neighbor Strategy to deal with the generalization on unseen graphs. Extensive experiments on eight public node-level and graph-level datasets demonstrate the generality and robustness of our proposed method. Compared to the FP32 models, our method can achieve up to $18.8\times$ (i.e., 1.70bits) compression ratio with negligible accuracy degradation. Moreover, compared to the state-of-the-art quantization method, our method can achieve up to $11.4\%$ and $9.5\%$ accuracy improvements on the node-level and graph-level tasks, respectively, and up to $2\times$ speedup on a dedicated hardware accelerator.
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https://openreview.net/pdf/c82dac44f9d2bed2286d39fa96c8dde717f135ef.pdf
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Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top
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https://openreview.net/forum?id=pfuqQQCB34
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https://openreview.net/forum?id=pfuqQQCB34
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Eduard Gorbunov,Samuel Horváth,Peter Richtárik,Gauthier Gidel
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ICLR 2023,Poster
|
Byzantine-robustness has been gaining a lot of attention due to the growth of the interest in collaborative and federated learning. However, many fruitful directions, such as the usage of variance reduction for achieving robustness and communication compression for reducing communication costs, remain weakly explored in the field. This work addresses this gap and proposes Byz-VR-MARINA -- a new Byzantine-tolerant method with variance reduction and compression. A key message of our paper is that variance reduction is key to fighting Byzantine workers more effectively. At the same time, communication compression is a bonus that makes the process more communication efficient. We derive theoretical convergence guarantees for Byz-VR-MARINA outperforming previous state-of-the-art for general non-convex and Polyak-Lojasiewicz loss functions. Unlike the concurrent Byzantine-robust methods with variance reduction and/or compression, our complexity results are tight and do not rely on restrictive assumptions such as boundedness of the gradients or limited compression. Moreover, we provide the first analysis of a Byzantine-tolerant method supporting non-uniform sampling of stochastic gradients. Numerical experiments corroborate our theoretical findings.
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https://openreview.net/pdf/628ed3086a3fa5385dafa18893069cc3b950bc45.pdf
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Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning
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https://openreview.net/forum?id=Nk2pDtuhTq
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https://openreview.net/forum?id=Nk2pDtuhTq
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Zhen Wang,Rameswar Panda,Leonid Karlinsky,Rogerio Feris,Huan Sun,Yoon Kim
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ICLR 2023,Poster
|
Prompt tuning, in which a base pretrained model is adapted to each task via conditioning on learned prompt vectors, has emerged as a promising approach for efficiently adapting large language models to multiple downstream tasks. However, existing methods typically learn soft prompt vectors from scratch, and it has not been clear how to exploit the rich cross-task knowledge with prompt vectors in a multitask learning setting. We propose multitask prompt tuning (MPT), which first learns a single transferable prompt by distilling knowledge from multiple task-specific source prompts. We then learn multiplicative low rank updates to this shared prompt to efficiently adapt it to each downstream target task. Extensive experiments on 23 NLP datasets demonstrate that our proposed approach outperforms the state-of-the-art methods, including the full finetuning baseline in some cases, despite only tuning $0.035\%$ as many task-specific parameters.
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https://openreview.net/pdf/7506d3076a848ae366fb1e6d0213bf0328ddd8aa.pdf
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Better Generative Replay for Continual Federated Learning
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https://openreview.net/forum?id=cRxYWKiTan
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https://openreview.net/forum?id=cRxYWKiTan
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Daiqing Qi,Handong Zhao,Sheng Li
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ICLR 2023,Poster
|
Federated Learning (FL) aims to develop a centralized server that learns from distributed clients via communications without accessing the clients’ local data. However, existing works mainly focus on federated learning in a single task sce- nario with static data. In this paper, we introduce the continual federated learning (CFL) problem, where clients incrementally learn new tasks and history data can- not be stored due to certain reasons, such as limited storage and data retention policy 1. Generative replay (GR) based methods are effective for continual learning without storing history data. However, we fail when trying to intuitively adapt GR models for this setting. By analyzing the behaviors of clients during training, we find the unstable training process caused by distributed training on non-IID data leads to a notable performance degradation. To address this problem, we propose our FedCIL model with two simple but effective solutions: 1. model consolidation and 2. consistency enforcement. Experimental results on multiple benchmark datasets demonstrate that our method significantly outperforms baselines. Code is available at: https://github.com/daiqing98/FedCIL.
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https://openreview.net/pdf/615fb01c6988a5eceaf1dbbe4113fd17ab8cc9f4.pdf
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Generative Modelling with Inverse Heat Dissipation
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https://openreview.net/forum?id=4PJUBT9f2Ol
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https://openreview.net/forum?id=4PJUBT9f2Ol
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Severi Rissanen,Markus Heinonen,Arno Solin
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ICLR 2023,Poster
|
While diffusion models have shown great success in image generation, their noise-inverting generative process does not explicitly consider the structure of images, such as their inherent multi-scale nature. Inspired by diffusion models and the empirical success of coarse-to-fine modelling, we propose a new diffusion-like model that generates images through stochastically reversing the heat equation, a PDE that locally erases fine-scale information when run over the 2D plane of the image. We interpret the solution of the forward heat equation with constant additive noise as a variational approximation in the diffusion latent variable model. Our new model shows emergent qualitative properties not seen in standard diffusion models, such as disentanglement of overall colour and shape in images. Spectral analysis on natural images highlights connections to diffusion models and reveals an implicit coarse-to-fine inductive bias in them.
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https://openreview.net/pdf/40788e42963a1dbec8dc274094381e704205a2be.pdf
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Self-supervision through Random Segments with Autoregressive Coding (RandSAC)
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https://openreview.net/forum?id=Ubc74gTVo3
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https://openreview.net/forum?id=Ubc74gTVo3
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Tianyu Hua,Yonglong Tian,Sucheng Ren,Michalis Raptis,Hang Zhao,Leonid Sigal
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ICLR 2023,Poster
|
Inspired by the success of self-supervised autoregressive representation learning in natural language (GPT and its variants), and advances in recent visual architecture design with Vision Transformers (ViTs), in this paper, we explore the effects various design choices have on the success of applying such training strategies for visual feature learning. Specifically, we introduce a novel strategy that we call Random Segments with Autoregressive Coding (RandSAC). In RandSAC, we group patch representations (image tokens) into hierarchically arranged segments; within each segment, tokens are predicted in parallel, similar to BERT, while across segment predictions are sequential, similar to GPT. We illustrate that randomized serialization of the segments significantly improves the performance and results in distribution over spatially-long (across-segments) and -short (within-segment) predictions which are effective for feature learning. We illustrate the pertinence of these design choices and explore alternatives on a number of datasets (e.g., CIFAR10, ImageNet). While our pre-training strategy works with vanilla Transformer, we also propose a conceptually simple, but highly effective, addition to the decoder that allows learnable skip-connections to encoder feature layers, which further improves the performance.
|
https://openreview.net/pdf/4e53d24d80bcf1d31e7eea1d99a647f689b2bf87.pdf
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Transformer-Patcher: One Mistake Worth One Neuron
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https://openreview.net/forum?id=4oYUGeGBPm
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https://openreview.net/forum?id=4oYUGeGBPm
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Zeyu Huang,Yikang Shen,Xiaofeng Zhang,Jie Zhou,Wenge Rong,Zhang Xiong
|
ICLR 2023,Poster
|
Large Transformer-based Pretrained Language Models (PLMs) dominate almost all Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes from time to time. For a model deployed in an industrial environment, fixing these mistakes quickly and robustly is vital to improve user experiences. Previous works formalize such problems as Model Editing (ME) and mostly focus on fixing one mistake. However, the one-mistake-fixing scenario is not an accurate abstraction of the real-world challenge. In the deployment of AI services, there are ever-emerging mistakes, and the same mistake may recur if not corrected in time. Thus a preferable solution is to rectify the mistakes as soon as they appear nonstop. Therefore, we extend the existing ME into the Sequential Model Editing (SME) to help develop more practical editing methods. Our study shows that current ME methods either fail to make a sequence of edits or to remember previous edits. We then introduce Transformer-Patcher, a novel model editor that can shift the behavior of transformer-based models by simply adding and training a few neurons in the last Feed-Forward Network layer. Experimental results on both classification and generation tasks show that Transformer-Patcher can successively correct up to thousands of errors (Reliability) and generalize to their equivalent inputs (Generality) while retaining the model’s accuracy on irrelevant inputs (Locality). Our method outperforms previous fine-tuning and HyperNetwork-based methods and achieves state-of-the-art performance for Sequential Model Editing (SME).
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https://openreview.net/pdf/333a3ae8305ed6fdef22d29e567970ee0060978d.pdf
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Sharper Bounds for Uniformly Stable Algorithms with Stationary Mixing Process
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https://openreview.net/forum?id=8E5Yazboyh
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https://openreview.net/forum?id=8E5Yazboyh
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Shi Fu,Yunwen Lei,Qiong Cao,Xinmei Tian,Dacheng Tao
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ICLR 2023,Poster
|
Generalization analysis of learning algorithms often builds on a critical assumption that training examples are independently and identically distributed, which is often violated in practical problems such as time series prediction. In this paper, we use algorithmic stability to study the generalization performance of learning algorithms with $\psi$-mixing data, where the dependency between observations weakens over time. We show uniformly stable algorithms guarantee high-probability generalization bounds of the order $O(1/\sqrt{n})$ (within a logarithmic factor), where $n$ is the sample size. We apply our general result to specific algorithms including regularization schemes, stochastic gradient descent and localized iterative regularization, and develop excess population risk bounds for learning with $\psi$-mixing data. Our analysis builds on a novel moment bound for weakly-dependent random variables on a $\varphi$-mixing sequence and a novel error decomposition of generalization error.
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https://openreview.net/pdf/960ab88f57531b40e2fb8ef0c415c93bde9c93f4.pdf
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Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models
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https://openreview.net/forum?id=UrEwJebCxk
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https://openreview.net/forum?id=UrEwJebCxk
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Kaiyue Wen,Jiaye Teng,Jingzhao Zhang
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ICLR 2023,Poster
|
Studies on benign overfitting provide insights for the success of overparameterized deep learning models. In this work, we examine whether overfitting is truly benign in real-world classification tasks. We start with the observation that a ResNet model overfits benignly on Cifar10 but not benignly on ImageNet. To understand why benign overfitting fails in the ImageNet experiment, we theoretically analyze benign overfitting under a more restrictive setup where the number of parameters is not significantly larger than the number of data points. Under this mild overparameterization setup, our analysis identifies a phase change: unlike in the previous heavy overparameterization settings, benign overfitting can now fail in the presence of label noise. Our analysis explains our empirical observations, and is validated by a set of control experiments with ResNets. Our work highlights the importance of understanding implicit bias in underfitting regimes as a future direction.
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https://openreview.net/pdf/a5c1d6dcdd8cc221428ba2d2ca0ae27b5526f529.pdf
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Predictive Inference with Feature Conformal Prediction
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https://openreview.net/forum?id=0uRm1YmFTu
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https://openreview.net/forum?id=0uRm1YmFTu
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Jiaye Teng,Chuan Wen,Dinghuai Zhang,Yoshua Bengio,Yang Gao,Yang Yuan
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ICLR 2023,Poster
|
Conformal prediction is a distribution-free technique for establishing valid prediction intervals. Although conventionally people conduct conformal prediction in the output space, this is not the only possibility. In this paper, we propose feature conformal prediction, which extends the scope of conformal prediction to semantic feature spaces by leveraging the inductive bias of deep representation learning. From a theoretical perspective, we demonstrate that feature conformal prediction provably outperforms regular conformal prediction under mild assumptions. Our approach could be combined with not only vanilla conformal prediction, but also other adaptive conformal prediction methods. Apart from experiments on existing predictive inference benchmarks, we also demonstrate the state-of-the-art performance of the proposed methods on \textit{large-scale} tasks such as ImageNet classification and Cityscapes image segmentation.
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https://openreview.net/pdf/25bebcfe147e7e7eecd28e707c7704dbd5cbd5f0.pdf
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Recon: Reducing Conflicting Gradients From the Root For Multi-Task Learning
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https://openreview.net/forum?id=ivwZO-HnzG_
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https://openreview.net/forum?id=ivwZO-HnzG_
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Guangyuan SHI,Qimai Li,Wenlong Zhang,Jiaxin Chen,Xiao-Ming Wu
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ICLR 2023,Poster
|
A fundamental challenge for multi-task learning is that different tasks may conflict with each other when they are solved jointly, and a cause of this phenomenon is conflicting gradients during optimization. Recent works attempt to mitigate the influence of conflicting gradients by directly altering the gradients based on some criteria. However, our empirical study shows that ``gradient surgery'' cannot effectively reduce the occurrence of conflicting gradients. In this paper, we take a different approach to reduce conflicting gradients from the root. In essence, we investigate the task gradients w.r.t. each shared network layer, select the layers with high conflict scores, and turn them to task-specific layers. Our experiments show that such a simple approach can greatly reduce the occurrence of conflicting gradients in the remaining shared layers and achieve better performance, with only a slight increase in model parameters in many cases. Our approach can be easily applied to improve various state-of-the-art methods including gradient manipulation methods and branched architecture search methods. Given a network architecture (e.g., ResNet18), it only needs to search for the conflict layers once, and the network can be modified to be used with different methods on the same or even different datasets to gain performance improvement. The source code is available at https://github.com/moukamisama/Recon.
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https://openreview.net/pdf/6326af1e7dafb7e641622b66fe683ac0d4ea2d62.pdf
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Measure the Predictive Heterogeneity
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https://openreview.net/forum?id=g2oB_k-18b
|
https://openreview.net/forum?id=g2oB_k-18b
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Jiashuo Liu,Jiayun Wu,Renjie Pi,Renzhe Xu,Xingxuan Zhang,Bo Li,Peng Cui
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ICLR 2023,Poster
|
As an intrinsic and fundamental property of big data, data heterogeneity exists in a variety of real-world applications, such as in agriculture, sociology, health care, etc. For machine learning algorithms, the ignorance of data heterogeneity will significantly hurt the generalization performance and the algorithmic fairness, since the prediction mechanisms among different sub-populations are likely to differ. In this work, we focus on the data heterogeneity that affects the prediction of machine learning models, and first formalize the Predictive Heterogeneity, which takes into account the model capacity and computational constraints. We prove that it can be reliably estimated from finite data with PAC bounds even in high dimensions. Additionally, we propose the Information Maximization (IM) algorithm, a bi-level optimization algorithm, to explore the predictive heterogeneity of data. Empirically, the explored predictive heterogeneity provides insights for sub-population divisions in agriculture, sociology, and object recognition, and leveraging such heterogeneity benefits the out-of-distribution generalization performance.
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https://openreview.net/pdf/e34d66bde391878e44d7632186ac2bdf879acda8.pdf
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Time to augment self-supervised visual representation learning
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https://openreview.net/forum?id=o8xdgmwCP8l
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https://openreview.net/forum?id=o8xdgmwCP8l
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Arthur Aubret,Markus R. Ernst,Céline Teulière,Jochen Triesch
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ICLR 2023,Poster
|
Biological vision systems are unparalleled in their ability to learn visual representations without supervision. In machine learning, self-supervised learning (SSL) has led to major advances in forming object representations in an unsupervised fashion. Such systems learn representations invariant to augmentation operations over images, like cropping or flipping. In contrast, biological vision systems exploit the temporal structure of the visual experience during natural interactions with objects. This gives access to “augmentations” not commonly used in SSL, like watching the same object from multiple viewpoints or against different backgrounds. Here, we systematically investigate and compare the potential benefits of such time-based augmentations during natural interactions for learning object categories. Our results show that incorporating time-based augmentations achieves large performance gains over state-of-the-art image augmentations. Specifically, our analyses reveal that: 1) 3-D object manipulations drastically improve the learning of object categories; 2) viewing objects against changing backgrounds is important for learning to discard background-related information from the latent representation. Overall, we conclude that time-based augmentations during natural interactions with objects can substantially improve self-supervised learning, narrowing the gap between artificial and biological vision systems.
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https://openreview.net/pdf/cc6a431132a9180908d245cfa70d30cd097b3152.pdf
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Towards Lightweight, Model-Agnostic and Diversity-Aware Active Anomaly Detection
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https://openreview.net/forum?id=-vKlt84fHs
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https://openreview.net/forum?id=-vKlt84fHs
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Xu Zhang,Yuan Zhao,Ziang Cui,Liqun Li,Shilin He,Qingwei Lin,Yingnong Dang,Saravan Rajmohan,Dongmei Zhang
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ICLR 2023,Poster
|
Active Anomaly Discovery (AAD) is flourishing in the anomaly detection research area, which aims to incorporate analysts’ feedback into unsupervised anomaly detectors. However, existing AAD approaches usually prioritize the samples with the highest anomaly scores for user labeling, which hinders the exploration of anomalies that were initially ranked lower. Besides, most existing AAD approaches are specially tailored for a certain unsupervised detector, making it difficult to extend to other detection models. To tackle these problems, we propose a lightweight, model-agnostic and diversity-aware AAD method, named LMADA. In LMADA, we design a diversity-aware sample selector powered by Determinantal Point Process (DPP). It considers the diversity of samples in addition to their anomaly scores for feedback querying. Furthermore, we propose a model-agnostic tuner. It approximates diverse unsupervised detectors with a unified proxy model, based on which the feedback information is incorporated by a lightweight non-linear representation adjuster. Through extensive experiments on 8 public datasets, LMADA achieved 74% F1-Score improvement on average, outperforming other comparative AAD approaches. Besides, LMADA can also achieve significant performance boosting under any unsupervised detectors.
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https://openreview.net/pdf/ea37f32b32fc362c6fe8adbf6720a9b0a5fe2b48.pdf
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Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots
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https://openreview.net/forum?id=AwWaBXLIJE
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https://openreview.net/forum?id=AwWaBXLIJE
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Wei Hung,Bo Kai Huang,Ping-Chun Hsieh,Xi Liu
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ICLR 2023,Poster
|
Many real-world continuous control problems are in the dilemma of weighing the pros and cons, multi-objective reinforcement learning (MORL) serves as a generic framework of learning control policies for different preferences over objectives. However, the existing MORL methods either rely on multiple passes of explicit search for finding the Pareto front and therefore are not sample-efficient, or utilizes a shared policy network for coarse knowledge sharing among policies. To boost the sample efficiency of MORL, we propose $Q$-Pensieve, a policy improvement scheme that stores a collection of $Q$-snapshots to jointly determine the policy update direction and thereby enables data sharing at the policy level. We show that $Q$-Pensieve can be naturally integrated with soft policy iteration with convergence guarantee. To substantiate this concept, we propose the technique of $Q$ replay buffer, which stores the learned $Q$-networks from the past iterations, and arrive at a practical actor-critic implementation. Through extensive experiments and an ablation study, we demonstrate that with much fewer samples, the proposed algorithm can outperform the benchmark MORL methods on a variety of MORL benchmark tasks.
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https://openreview.net/pdf/6f04c0806c254e1bc22ec280e54571a782628ae0.pdf
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Variance-Aware Sparse Linear Bandits
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https://openreview.net/forum?id=tkwP32nsEq
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https://openreview.net/forum?id=tkwP32nsEq
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Yan Dai,Ruosong Wang,Simon Shaolei Du
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ICLR 2023,Poster
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It is well-known that for sparse linear bandits, when ignoring the dependency on sparsity which is much smaller than the ambient dimension, the worst-case minimax regret is $\widetilde{\Theta}\left(\sqrt{dT}\right)$ where $d$ is the ambient dimension and $T$ is the number of rounds. On the other hand, in the benign setting where there is no noise and the action set is the unit sphere, one can use divide-and-conquer to achieve $\widetilde{\mathcal O}(1)$ regret, which is (nearly) independent of $d$ and $T$. In this paper, we present the first variance-aware regret guarantee for sparse linear bandits: $\widetilde{\mathcal O}\left(\sqrt{d\sum_{t=1}^T \sigma_t^2} + 1\right)$, where $\sigma_t^2$ is the variance of the noise at the $t$-th round. This bound naturally interpolates the regret bounds for the worst-case constant-variance regime (i.e., $\sigma_t \equiv \Omega(1)$) and the benign deterministic regimes (i.e., $\sigma_t \equiv 0$). To achieve this variance-aware regret guarantee, we develop a general framework that converts any variance-aware linear bandit algorithm to a variance-aware algorithm for sparse linear bandits in a "black-box" manner. Specifically, we take two recent algorithms as black boxes to illustrate that the claimed bounds indeed hold, where the first algorithm can handle unknown-variance cases and the second one is more efficient.
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https://openreview.net/pdf/515823c3e758168be3caad5fec0b7c8a04637995.pdf
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CircNet: Meshing 3D Point Clouds with Circumcenter Detection
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https://openreview.net/forum?id=zQWqV2tzDv
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https://openreview.net/forum?id=zQWqV2tzDv
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Huan Lei,Ruitao Leng,Liang Zheng,Hongdong Li
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ICLR 2023,Poster
|
Reconstructing 3D point clouds into triangle meshes is a key problem in computational geometry and surface reconstruction. Point cloud triangulation solves this problem by providing edge information to the input points. Since no vertex interpolation is involved, it is beneficial to preserve sharp details on the surface. Taking advantage of learning-based techniques in triangulation, existing methods enumerate the complete combinations of candidate triangles, which is both complex and inefficient. In this paper, we leverage the duality between a triangle and its circumcenter, and introduce a deep neural network that detects the circumcenters to achieve point cloud triangulation. Specifically, we introduce multiple anchor priors to divide the neighborhood space of each point. The neural network then learns to predict the presences and locations of circumcenters under the guidance of those anchors. We extract the triangles dual to the detected circumcenters to form a primitive mesh, from which an edge-manifold mesh is produced via simple post-processing. Unlike existing learning-based triangulation methods, the proposed method bypasses an exhaustive enumeration of triangle combinations and local surface parameterization. We validate the efficiency, generalization, and robustness of our method on prominent datasets of both watertight and open surfaces. The code and trained models are provided at \url{https://github.com/Ruitao-L/CircNet}.
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https://openreview.net/pdf/f1b1428f07774521292a5203739b985acc12400f.pdf
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In-sample Actor Critic for Offline Reinforcement Learning
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https://openreview.net/forum?id=dfDv0WU853R
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https://openreview.net/forum?id=dfDv0WU853R
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Hongchang Zhang,Yixiu Mao,Boyuan Wang,Shuncheng He,Yi Xu,Xiangyang Ji
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ICLR 2023,Poster
|
Offline reinforcement learning suffers from out-of-distribution issue and extrapolation error. Most methods penalize the out-of-distribution state-action pairs or regularize the trained policy towards the behavior policy but cannot guarantee to get rid of extrapolation error. We propose In-sample Actor Critic (IAC) which utilizes sampling-importance resampling to execute in-sample policy evaluation. IAC only uses the target Q-values of the actions in the dataset to evaluate the trained policy, thus avoiding extrapolation error. The proposed method performs unbiased policy evaluation and has a lower variance than importance sampling in many cases. Empirical results show that IAC obtains competitive performance compared to the state-of-the-art methods on Gym-MuJoCo locomotion domains and much more challenging AntMaze domains.
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https://openreview.net/pdf/036a9dbc3c8ddf37986fc41037cbd871a2d0b950.pdf
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Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction
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https://openreview.net/forum?id=CGBCTp2M6lA
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https://openreview.net/forum?id=CGBCTp2M6lA
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Daehee Park,Hobin Ryu,Yunseo Yang,Jegyeong Cho,Jiwon Kim,Kuk-Jin Yoon
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ICLR 2023,Poster
|
Understanding the interaction between multiple agents is crucial for realistic vehicle trajectory prediction.
Existing methods have attempted to infer the interaction from the observed past trajectories of agents using pooling, attention, or graph-based methods, which rely on a deterministic approach.
However, these methods can fail under complex road structures, as they cannot predict various interactions that may occur in the future.
In this paper, we propose a novel approach that uses lane information to predict a stochastic future relationship among agents.
To obtain a coarse future motion of agents, our method first predicts the probability of lane-level waypoint occupancy of vehicles.
We then utilize the temporal probability of passing adjacent lanes for each agent pair, assuming that agents passing adjacent lanes will highly interact.
We also model the interaction using a probabilistic distribution, which allows for multiple possible future interactions.
The distribution is learned from the posterior distribution of interaction obtained from ground truth future trajectories.
We validate our method on popular trajectory prediction datasets: nuScenes and Argoverse.
The results show that the proposed method brings remarkable performance gain in prediction accuracy, and achieves state-of-the-art performance in long-term prediction benchmark dataset.
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https://openreview.net/pdf/26afa69e0c70599af069b4e1fd67c5256d02890a.pdf
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LMSeg: Language-guided Multi-dataset Segmentation
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https://openreview.net/forum?id=P44WPn1_aJV
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https://openreview.net/forum?id=P44WPn1_aJV
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Qiang Zhou,Yuang Liu,Chaohui Yu,Jingliang Li,Zhibin Wang,Fan Wang
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ICLR 2023,Poster
|
It’s a meaningful and attractive topic to build a general and inclusive segmentation model that can recognize more categories in various scenarios. A straightforward way is to combine the existing fragmented segmentation datasets and train a multi-dataset network. However, there are two major issues with multi-dataset segmentation: (i) the inconsistent taxonomy demands manual reconciliation to construct a unified taxonomy; (ii) the inflexible one-hot common taxonomy causes time-consuming model retraining and defective supervision of unlabeled categories. In this paper, we investigate the multi-dataset segmentation and propose a scalable Language-guided Multi-dataset Segmentation framework, dubbed LMSeg, which supports both semantic and panoptic segmentation. Specifically, we introduce a pretrained text encoder to map the category names to a text embedding space as a unified taxonomy, instead of using inflexible one-hot label. The model dynamically aligns the segment queries with the category embeddings. Instead of relabeling each dataset with the unified taxonomy, a category-guided decoding module is designed to dynamically guide predictions to each dataset’s taxonomy. Furthermore, we adopt a dataset-aware augmentation strategy that assigns each dataset a specific image augmentation pipeline, which can suit the proper-
ties of images from different datasets. Extensive experiments demonstrate that our method achieves significant improvements on four segmentation datasets and three panoptic datasets, while the ablation study evaluates the effectiveness of each component.
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https://openreview.net/pdf/e122f403769a195b33e2acb0ccb9664e5aa5d846.pdf
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RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data
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https://openreview.net/forum?id=G1H4NSATlr
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https://openreview.net/forum?id=G1H4NSATlr
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Sangwoo Mo,Jong-Chyi Su,Chih-Yao Ma,Mido Assran,Ishan Misra,Licheng Yu,Sean Bell
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ICLR 2023,Poster
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Semi-supervised learning aims to train a model using limited labels. State-of-the-art semi-supervised methods for image classification such as PAWS rely on self-supervised representations learned with large-scale unlabeled but curated data. However, PAWS is often less effective when using real-world unlabeled data that is uncurated, e.g., contains out-of-class data. We propose RoPAWS, a robust extension of PAWS that can work with real-world unlabeled data. We first reinterpret PAWS as a generative classifier that models densities using kernel density estimation. From this probabilistic perspective, we calibrate its prediction based on the densities of labeled and unlabeled data, which leads to a simple closed-form solution from the Bayes' rule. We demonstrate that RoPAWS significantly improves PAWS for uncurated Semi-iNat by +5.3% and curated ImageNet by +0.4%.
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https://openreview.net/pdf/c586bb7922f76665c6cafd577bdef3f4b80b6830.pdf
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Treeformer: Dense Gradient Trees for Efficient Attention Computation
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https://openreview.net/forum?id=DWn1TEb2fK
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https://openreview.net/forum?id=DWn1TEb2fK
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Lovish Madaan,Srinadh Bhojanapalli,Himanshu Jain,Prateek Jain
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ICLR 2023,Poster
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Standard inference and training with transformer based architectures scale quadratically with input sequence length. This is prohibitively large for a variety of applications especially in web-page translation, query-answering etc. Consequently, several approaches have been developed recently to speedup attention computation by enforcing different attention structures such as sparsity, low-rank, approximating attention using kernels. In this work, we view attention computation as that of nearest neighbor retrieval, and use decision tree based hierarchical navigation to reduce the retrieval cost per query token from linear in sequence length to nearly logarithmic. Based on such hierarchical navigation, we design Treeformer which can use one of two efficient attention layers -- TF-Attention and TC-Attention. TF-Attention computes the attention in a fine-grained style, while TC-Attention is a coarse attention layer which also ensures that the gradients are "dense". To optimize such challenging discrete layers, we propose a two-level bootstrapped training method. Using extensive experiments on standard NLP benchmarks, especially for long-sequences, we demonstrate that our Treeformer architecture can be almost as accurate as baseline Transformer while using 30x lesser FLOPs in the attention layer. Compared to Linformer, the accuracy can be as much as 12% higher while using similar FLOPs in the attention layer.
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https://openreview.net/pdf/edaf2059b7ab33806603c66b3effcaf422ef6644.pdf
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ODAM: Gradient-based Instance-Specific Visual Explanations for Object Detection
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https://openreview.net/forum?id=kJWcI39kXY
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https://openreview.net/forum?id=kJWcI39kXY
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Chenyang ZHAO,Antoni B. Chan
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ICLR 2023,Poster
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We propose the Gradient-weighted Object Detector Activation Mapping (Grad-ODAM), a visualized explanation technique for interpreting the predictions of object detectors. Utilizing the gradients of detector targets flowing into the intermediate feature maps, Grad-ODAM produces heat maps that show the influence of regions on the detector's decision. Compared to previous classification activation mapping works, Grad-ODAM generates instance-specific explanations rather than class-specific ones. We show that Grad-ODAM is applicable to both one-stage detectors such as FCOS and two-stage detectors such as Faster R-CNN, and produces higher-quality visual explanations than the state-of-the-art both effectively and efficiently. We next propose a training scheme, ODAM-Train, to improve the explanation ability on object discrimination of the detector through encouraging consistency between explanations for detections on the same object, and distinct explanations for detections on different objects. Based on the heat maps produced by Grad-ODAM with ODAM-Train, we propose ODAM-NMS, which considers the information of the model's explanation for each prediction to distinguish the duplicate detected objects. We present a detailed analysis of the visualized explanations of detectors and carry out extensive experiments to validate the effectiveness of the proposed ODAM.
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https://openreview.net/pdf/dafd4dbfa124993610ceda1a2409041764a1726c.pdf
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Toward Adversarial Training on Contextualized Language Representation
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https://openreview.net/forum?id=xZD10GhCvM
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https://openreview.net/forum?id=xZD10GhCvM
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Hongqiu Wu,Yongxiang Liu,Hanwen Shi,hai zhao,Min Zhang
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ICLR 2023,Poster
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Beyond the success story of adversarial training (AT) in the recent text domain on top of pre-trained language models (PLMs), our empirical study showcases the inconsistent gains from AT on some tasks, e.g. commonsense reasoning, named entity recognition. This paper investigates AT from the perspective of the contextualized language representation outputted by PLM encoders. We find the current AT attacks lean to generate sub-optimal adversarial examples that can fool the decoder part but have a minor effect on the encoder. However, we find it necessary to effectively deviate the latter one to allow AT to gain. Based on the observation, we propose simple yet effective \textit{Contextualized representation-Adversarial Training} (CreAT), in which the attack is explicitly optimized to deviate the contextualized representation of the encoder. It allows a global optimization of adversarial examples that can fool the entire model. We also find CreAT gives rise to a better direction to optimize the adversarial examples, to let them less sensitive to hyperparameters. Compared to AT, CreAT produces consistent performance gains on a wider range of tasks and is proven to be more effective for language pre-training where only the encoder part is kept for downstream tasks. We achieve the new state-of-the-art performances on a series of challenging benchmarks, e.g. AdvGLUE (59.1 $ \rightarrow $ 61.1), HellaSWAG (93.0 $ \rightarrow $ 94.9), ANLI (68.1 $ \rightarrow $ 69.3).
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https://openreview.net/pdf/f99847b969c62de5c7cf014d27a5a81631b43597.pdf
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Gromov-Wasserstein Autoencoders
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https://openreview.net/forum?id=sbS10BCtc7
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https://openreview.net/forum?id=sbS10BCtc7
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Nao Nakagawa,Ren Togo,Takahiro Ogawa,Miki Haseyama
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ICLR 2023,Poster
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Variational Autoencoder (VAE)-based generative models offer flexible representation learning by incorporating meta-priors, general premises considered beneficial for downstream tasks. However, the incorporated meta-priors often involve ad-hoc model deviations from the original likelihood architecture, causing undesirable changes in their training. In this paper, we propose a novel representation learning method, Gromov-Wasserstein Autoencoders (GWAE), which directly matches the latent and data distributions using the variational autoencoding scheme. Instead of likelihood-based objectives, GWAE models minimize the Gromov-Wasserstein (GW) metric between the trainable prior and given data distributions. The GW metric measures the distance structure-oriented discrepancy between distributions even with different dimensionalities, which provides a direct measure between the latent and data spaces. By restricting the prior family, we can introduce meta-priors into the latent space without changing their objective. The empirical comparisons with VAE-based models show that GWAE models work in two prominent meta-priors, disentanglement and clustering, with their GW objective unchanged.
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https://openreview.net/pdf/d1b76d3b5d60e6f2f19e3c6ab291ba6ed6ae3523.pdf
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Optimal Activation Functions for the Random Features Regression Model
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https://openreview.net/forum?id=ltWade-cpK
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https://openreview.net/forum?id=ltWade-cpK
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Jianxin Wang,José Bento
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ICLR 2023,Poster
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The asymptotic mean squared test error and sensitivity of the Random Features Regression model (RFR) have been recently studied. We build on this work and identify in closed-form the family of Activation Functions (AFs) that minimize a combination of the test error and sensitivity of the RFR under different notions of functional parsimony. We find scenarios under which the optimal AFs are linear, saturated linear functions, or expressible in terms of Hermite polynomials. Finally, we show how using optimal AFs impacts well established properties of the RFR model, such as its double descent curve, and the dependency of its optimal regularization parameter on the observation noise level.
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https://openreview.net/pdf/8895967b0a0cc46d0f4328a9c58e36e5da0fec6b.pdf
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Improving Object-centric Learning with Query Optimization
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https://openreview.net/forum?id=_-FN9mJsgg
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https://openreview.net/forum?id=_-FN9mJsgg
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Baoxiong Jia,Yu Liu,Siyuan Huang
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ICLR 2023,Poster
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The ability to decompose complex natural scenes into meaningful object-centric abstractions lies at the core of human perception and reasoning. In the recent culmination of unsupervised object-centric learning, the Slot-Attention module has played an important role with its simple yet effective design and fostered many powerful variants. These methods, however, have been exceedingly difficult to train
without supervision and are ambiguous in the notion of object, especially for complex natural scenes. In this paper, we propose to address these issues by investigating the potential of learnable queries as initializations for Slot-Attention learning, uniting it with efforts from existing attempts on improving Slot-Attention learning with bi-level optimization. With simple code adjustments on Slot-Attention, our model, Bi-level Optimized Query Slot Attention, achieves state-of-the-art results on 3 challenging synthetic and 7 complex real-world datasets in unsupervised image segmentation and reconstruction, outperforming previous baselines by a large margin. We provide thorough ablative studies to validate the necessity and effectiveness of our design. Additionally, our model exhibits great potential for concept binding and zero-shot learning. Our work is made publicly available at https://bo-qsa.github.io.
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https://openreview.net/pdf/d5ec9ca4a9c3d9b9a0b66373194a2f787d2e515c.pdf
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Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks
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https://openreview.net/forum?id=zH9GcZ3ZGXu
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https://openreview.net/forum?id=zH9GcZ3ZGXu
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Sravanti Addepalli,Anshul Nasery,Venkatesh Babu Radhakrishnan,Praneeth Netrapalli,Prateek Jain
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ICLR 2023,Poster
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Deep Neural Networks are known to be brittle to even minor distribution shifts compared to the training distribution. While one line of work has demonstrated that \emph{Simplicity Bias} (SB) of DNNs -- bias towards learning only the simplest features -- is a key reason for this brittleness, another recent line of work has surprisingly found that diverse/ complex features are indeed learned by the backbone, and their brittleness is due to the linear classification head relying primarily on the simplest features. To bridge the gap between these two lines of work, we first hypothesize and verify that while SB may not altogether preclude learning complex features, it amplifies simpler features over complex ones. Namely, simple features are replicated several times in the learned representations while complex features might not be replicated. This phenomenon, we term \emph{Feature Replication Hypothesis}, coupled with the \emph{Implicit Bias} of SGD to converge to maximum margin solutions in the feature space, leads the models to rely mostly on the simple features for classification. To mitigate this bias, we propose \emph{Feature Reconstruction Regularizer (FRR)} to ensure that the learned features can be reconstructed back from the logits. The use of \emph{FRR} in linear layer training (\emph{FRR-L}) encourages the use of more diverse features for classification. We further propose to finetune the full network by freezing the weights of the linear layer trained using \emph{FRR-L}, to refine the learned features, making them more suitable for classification. Using this simple solution, we demonstrate up to 15\% gains in OOD accuracy on the recently introduced semi-synthetic datasets with extreme distribution shifts. Moreover, we demonstrate noteworthy gains over existing SOTA methods on the standard OOD benchmark DomainBed as well.
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https://openreview.net/pdf/d04b89a2199e2c436eabe69c7d7b984b0f21db55.pdf
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EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model
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https://openreview.net/forum?id=xQAjSr64PTc
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https://openreview.net/forum?id=xQAjSr64PTc
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Yifu Yuan,Jianye HAO,Fei Ni,Yao Mu,YAN ZHENG,Yujing Hu,Jinyi Liu,Yingfeng Chen,Changjie Fan
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ICLR 2023,Poster
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Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks. Previous works focused on the pre-training in a model-free manner while lacking the study of transition dynamics modeling that leaves a large space for the improvement of sample efficiency in downstream tasks. To this end, we propose an Efficient Unsupervised Reinforcement Learning Framework with Multi-choice Dynamics model (EUCLID), which introduces a novel model-fused paradigm to jointly pre-train the dynamics model and unsupervised exploration policy in the pre-training phase, thus better leveraging the environmental samples and improving the downstream task sampling efficiency. However, constructing a generalizable model which captures the local dynamics under different behaviors remains a challenging problem. We introduce the multi-choice dynamics model that covers different local dynamics under different behaviors concurrently, which uses different heads to learn the state transition under different behaviors during unsupervised pre-training and selects the most appropriate head for prediction in the downstream task. Experimental results in the manipulation and locomotion domains demonstrate that EUCLID achieves state-of-the-art performance with high sample efficiency, basically solving the state-based URLB benchmark and reaching a mean normalized score of 104.0±1.2% in downstream tasks with 100k fine-tuning steps, which is equivalent to DDPG’s performance at 2M interactive steps with 20× more data. More visualization videos are released on our homepage.
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https://openreview.net/pdf/e0e5ffe815fc27390865398ac70e8e25250b12a7.pdf
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Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition
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https://openreview.net/forum?id=lj1Eb1OPeNw
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https://openreview.net/forum?id=lj1Eb1OPeNw
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Junyan Wang,Zhenhong Sun,Yichen Qian,Dong Gong,Xiuyu Sun,Ming Lin,Maurice Pagnucco,Yang Song
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ICLR 2023,Poster
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3D convolution neural networks (CNNs) have been the prevailing option for video recognition. To capture the temporal information, 3D convolutions are computed along the sequences, leading to cubically growing and expensive computations. To reduce the computational cost, previous methods resort to manually designed 3D/2D CNN structures with approximations or automatic search, which sacrifice the modeling ability or make training time-consuming. In this work, we propose to automatically design efficient 3D CNN architectures via a novel training-free neural architecture search approach tailored for 3D CNNs considering the model complexity. To measure the expressiveness of 3D CNNs efficiently, we formulate a 3D CNN as an information system and derive an analytic entropy score, based on the Maximum Entropy Principle. Specifically, we propose a spatio-temporal entropy score (STEntr-Score) with a refinement factor to handle the discrepancy of visual information in spatial and temporal dimensions, through dynamically leveraging the correlation between the feature map size and kernel size depth-wisely. Highly efficient and expressive 3D CNN architectures, i.e., entropy-based 3D CNNs (E3D family), can then be efficiently searched by maximizing the STEntr-Score under a given computational budget, via an evolutionary algorithm without training the network parameters. Extensive experiments on Something-Something V1&V2 and Kinetics400 demonstrate that the E3D family achieves state-of-the-art performance with higher computational efficiency.
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https://openreview.net/pdf/2e1153f75f8b0e04afc079a8e88bdc6fd522125c.pdf
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Cycle to Clique (Cy2C) Graph Neural Network: A Sight to See beyond Neighborhood Aggregation
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https://openreview.net/forum?id=7d-g8KozkiE
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https://openreview.net/forum?id=7d-g8KozkiE
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Yun Young Choi,Sun Woo Park,Youngho Woo,U Jin Choi
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ICLR 2023,Poster
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Graph neural networks have been successfully adapted for learning vector representations of graphs through various neighborhood aggregation schemes. Previous researches suggest, however, that they possess limitations in incorporating key non-Euclidean topological properties of graphs. This paper mathematically identifies the caliber of graph neural networks in classifying isomorphism classes of graphs with continuous node attributes up to their local topological properties. In light of these observations, we construct the Cycle to Clique graph neural network, a novel yet simple algorithm which topologically enriches the input data of conventional graph neural networks while preserving their architectural components. This method theoretically outperforms conventional graph neural networks in classifying isomorphism classes of graphs while ensuring comparable time complexity in representing random graphs. Empirical results further support that the novel algorithm produces comparable or enhanced results in classifying benchmark graph data sets compared to contemporary variants of graph neural networks.
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https://openreview.net/pdf/e003ecf31b7a0a59fef61751d113f740a0af773c.pdf
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Latent State Marginalization as a Low-cost Approach for Improving Exploration
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https://openreview.net/forum?id=b0UksKFcTOL
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https://openreview.net/forum?id=b0UksKFcTOL
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Dinghuai Zhang,Aaron Courville,Yoshua Bengio,Qinqing Zheng,Amy Zhang,Ricky T. Q. Chen
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ICLR 2023,Poster
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While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- is usually motivated from a probabilistic perspective, the use of deep probabilistic models have not gained much traction in practice due to their inherent complexity. In this work, we propose the adoption of latent variable policies within the MaxEnt framework, which we can provably approximate any policy distribution, and additionally, naturally emerges under the use of world models with a latent belief state.
We discuss why latent variable policies are difficult to train, how naive approaches can fail, and subsequently introduce a series of improvements centered around low-cost marginalization of the latent state, allowing us to make full use of the latent state at minimal additional cost. We instantiate our method under the actor-critic framework, marginalizing both the actor and critic. The resulting algorithm, referred to as Stochastic Marginal Actor-Critic (SMAC), is simple yet effective. We experimentally validate our method on continuous control tasks, showing that effective marginalization can lead to better exploration and more robust training. Our implementation is open sourced at https://github.com/zdhNarsil/Stochastic-Marginal-Actor-Critic.
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https://openreview.net/pdf/f2dd0f2e03f758d3b57efe5cb6cf0b05f6ce3fe3.pdf
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Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap
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https://openreview.net/forum?id=inU2quhGdNU
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https://openreview.net/forum?id=inU2quhGdNU
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Weiyang Liu,Longhui Yu,Adrian Weller,Bernhard Schölkopf
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ICLR 2023,Poster
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The neural collapse (NC) phenomenon describes an underlying geometric symmetry for deep neural networks, where both deeply learned features and classifiers converge to a simplex equiangular tight frame. It has been shown that both cross-entropy loss and mean square error can provably lead to NC. We remove NC's key assumption on the feature dimension and the number of classes, and then present a generalized neural collapse (GNC) hypothesis that effectively subsumes the original NC. Inspired by how NC characterizes the training target of neural networks, we decouple GNC into two objectives: minimal intra-class variability and maximal inter-class separability. We then use hyperspherical uniformity (which characterizes the degree of uniformity on the unit hypersphere) as a unified framework to quantify these two objectives. Finally, we propose a general objective -- hyperspherical uniformity gap (HUG), which is defined by the difference between inter-class and intra-class hyperspherical uniformity. HUG not only provably converges to GNC, but also decouples GNC into two separate objectives. Unlike cross-entropy loss that couples intra-class compactness and inter-class separability, HUG enjoys more flexibility and serves as a good alternative loss function. Empirical results show that HUG works well in terms of generalization and robustness.
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https://openreview.net/pdf/a4cfb809d566020b1780f37a7179260010d539ff.pdf
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MaskFusion: Feature Augmentation for Click-Through Rate Prediction via Input-adaptive Mask Fusion
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https://openreview.net/forum?id=QzbKH8nNq_V
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https://openreview.net/forum?id=QzbKH8nNq_V
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Chao Liao,Jianchao Tan,Jiyuan Jia,Yi Guo,Chengru Song
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ICLR 2023,Poster
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Click-through rate (CTR) prediction plays important role in the advertisement, recommendation, and retrieval applications. Given the feature set, how to fully utilize the information from the feature set is an active topic in deep CTR model designs. There are several existing deep CTR works focusing on feature interactions, feature attentions, and so on. They attempt to capture high-order feature interactions to enhance the generalization ability of deep CTR models. However, these works either suffer from poor high-order feature interaction modeling using DNN or ignore the balance between generalization and memorization during the recommendation. To mitigate these problems, we propose an adaptive feature fusion framework called MaskFusion, to additionally capture the explicit interactions between the input feature and the existing deep part structure of deep CTR models dynamically, besides the common feature interactions proposed in existing works. MaskFusion is an instance-aware feature augmentation method, which makes deep CTR models more personalized by assigning each feature with an instance-adaptive mask and fusing each feature with each hidden state vector in the deep part structure. MaskFusion can also be integrated into any existing deep CTR models flexibly. MaskFusion achieves state-of-the-art (SOTA) performance on all seven benchmarks deep CTR models with three public datasets.
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https://openreview.net/pdf/757eef90a6353230918779727bb076fa2e64885b.pdf
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Rethinking Self-Supervised Visual Representation Learning in Pre-training for 3D Human Pose and Shape Estimation
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https://openreview.net/forum?id=8U4joMeLRF
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https://openreview.net/forum?id=8U4joMeLRF
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Hongsuk Choi,Hyeongjin Nam,Taeryung Lee,Gyeongsik Moon,Kyoung Mu Lee
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ICLR 2023,Poster
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Recently, a few self-supervised representation learning (SSL) methods have outperformed the ImageNet classification pre-training for vision tasks such as object detection. However, its effects on 3D human body pose and shape estimation (3DHPSE) are open to question, whose target is fixed to a unique class, the human, and has an inherent task gap with SSL. We empirically study and analyze the effects of SSL and further compare it with other pre-training alternatives for 3DHPSE. The alternatives are 2D annotation-based pre-training and synthetic data pre-training, which share the motivation of SSL that aims to reduce the labeling cost. They have been widely utilized as a source of weak-supervision or fine-tuning, but have not been remarked as a pre-training source. SSL methods underperform the conventional ImageNet classification pre-training on multiple 3DHPSE benchmarks by 7.7% on average. In contrast, despite a much less amount of pre-training data, the 2D annotation-based pre-training improves accuracy on all benchmarks and shows faster convergence during fine-tuning. Our observations challenge the naive application of the current SSL pre-training to 3DHPSE and relight the value of other data types in the pre-training aspect.
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https://openreview.net/pdf/6017994942dfe60d2f4cb69be42afc4cd3675da8.pdf
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Learned Index with Dynamic $\epsilon$
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https://openreview.net/forum?id=UiaUEICawgw
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https://openreview.net/forum?id=UiaUEICawgw
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Daoyuan Chen,Wuchao Li,Yaliang Li,Bolin Ding,Kai Zeng,Defu Lian,Jingren Zhou
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ICLR 2023,Poster
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Index structure is a fundamental component in database and facilitates broad data retrieval applications. Recent learned index methods show superior performance by learning hidden yet useful data distribution with the help of machine learning, and provide a guarantee that the prediction error is no more than a pre-defined $\epsilon$. However, existing learned index methods adopt a fixed $\epsilon$ for all the learned segments, neglecting the diverse characteristics of different data localities. In this paper, we propose a mathematically-grounded learned index framework with dynamic $\epsilon$, which is efficient and pluggable to existing learned index methods. We theoretically analyze prediction error bounds that link $\epsilon$ with data characteristics for an illustrative learned index method. Under the guidance of the derived bounds, we learn how to vary $\epsilon$ and improve the index performance with a better space-time trade-off. Experiments with real-world datasets and several state-of-the-art methods demonstrate the efficiency, effectiveness and usability of the proposed framework.
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https://openreview.net/pdf/e4fe0b82f4dd996529fc3feab43a65cff8f4b634.pdf
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Boosting Multiagent Reinforcement Learning via Permutation Invariant and Permutation Equivariant Networks
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https://openreview.net/forum?id=OxNQXyZK-K8
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https://openreview.net/forum?id=OxNQXyZK-K8
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Jianye HAO,Xiaotian Hao,Hangyu Mao,Weixun Wang,Yaodong Yang,Dong Li,YAN ZHENG,Zhen Wang
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ICLR 2023,Poster
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The state space in Multiagent Reinforcement Learning (MARL) grows exponentially with the agent number. Such a curse of dimensionality results in poor scalability and low sample efficiency, inhibiting MARL for decades. To break this curse, we propose a unified agent permutation framework that exploits the permutation invariance (PI) and permutation equivariance (PE) inductive biases to reduce the multiagent state space. Our insight is that permuting the order of entities in the factored multiagent state space does not change the information. Specifically, we propose two novel implementations: a Dynamic Permutation Network (DPN) and a Hyper Policy Network (HPN). The core idea is to build separate entity-wise PI input and PE output network modules to connect the entity-factored state space and action space in an end-to-end way. DPN achieves such connections by two separate module selection networks, which consistently assign the same input module to the same input entity (guarantee PI) and assign the same output module to the same entity-related output (guarantee PE). To enhance the representation capability, HPN replaces the module selection networks of DPN with hypernetworks to directly generate the corresponding module weights. Extensive experiments in SMAC, Google Research Football and MPE validate that the proposed methods significantly boost the performance and the learning efficiency of existing MARL algorithms. Remarkably, in SMAC, we achieve 100% win rates in almost all hard and super-hard scenarios (never achieved before).
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https://openreview.net/pdf/12d1e22292ed56512748543827e4cf87c98d1534.pdf
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wav2tok: Deep Sequence Tokenizer for Audio Retrieval
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https://openreview.net/forum?id=v8Mi8KU6056
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https://openreview.net/forum?id=v8Mi8KU6056
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Adhiraj Banerjee,Vipul Arora
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ICLR 2023,Poster
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Search over audio sequences is a fundamental problem. In this paper, we propose a method to extract concise discrete representations for audio that can be used for efficient retrieval. Our motivation comes from orthography which represents speech of a given language in a concise and distinct discrete form. The proposed method, wav2tok, learns such representations for any kind of audio, speech or non-speech, from pairs of similar audio. wav2tok compresses the query and target sequences into shorter sequences of tokens that are faster to match. The learning method makes use of CTC loss and expectation-maximization algorithm, which are generally used for supervised automatic speech recognition and for learning discrete latent variables, respectively. Experiments show the consistent performance of wav2tok across two audio retrieval tasks: music search (query by humming) and speech search via audio query, outperforming state-of-the-art baselines.
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https://openreview.net/pdf/175c36e27f6914e6af51b7966e83acbc0f13a160.pdf
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PV3D: A 3D Generative Model for Portrait Video Generation
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https://openreview.net/forum?id=o3yygm3lnzS
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https://openreview.net/forum?id=o3yygm3lnzS
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Zhongcong Xu,Jianfeng Zhang,Jun Hao Liew,Wenqing Zhang,Song Bai,Jiashi Feng,Mike Zheng Shou
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ICLR 2023,Poster
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Recent advances in generative adversarial networks (GANs) have demonstrated the capabilities of generating stunning photo-realistic portrait images. While some prior works have applied such image GANs to unconditional 2D portrait video generation and static 3D portrait synthesis, there are few works successfully extending GANs for generating 3D-aware portrait videos. In this work, we propose PV3D, the first generative framework that can synthesize multi-view consistent portrait videos. Specifically, our method extends the recent static 3D-aware image GAN to the video domain by generalizing the 3D implicit neural representation to model the spatio-temporal space. To introduce motion dynamics into the generation process, we develop a motion generator by stacking multiple motion layers to generate motion features via modulated convolution. To alleviate motion ambiguities caused by camera/human motions, we propose a simple yet effective camera condition strategy for PV3D, enabling both temporal and multi-view consistent video generation. Moreover, PV3D introduces two discriminators for regularizing the spatial and temporal domains to ensure the plausibility of the generated portrait videos. These elaborated designs enable PV3D to generate 3D-aware motion-plausible portrait videos with high-quality appearance and geometry, significantly outperforming prior works. As a result, PV3D is able to support downstream applications such as static portrait animation and view-consistent motion editing. Code and models are available at https://showlab.github.io/pv3d.
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https://openreview.net/pdf/82ef7ccbfefbab9e2cb9eec97a0b60254a7efa92.pdf
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Characterizing the Influence of Graph Elements
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https://openreview.net/forum?id=51GXyzOKOp
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https://openreview.net/forum?id=51GXyzOKOp
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Zizhang Chen,Peizhao Li,Hongfu Liu,Pengyu Hong
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ICLR 2023,Poster
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Influence function, a method from the robust statistics, measures the changes of model parameters or some functions about model parameters with respect to the removal or modification of training instances. It is an efficient and useful post-hoc method for studying the interpretability of machine learning models without the need of expensive model re-training. Recently, graph convolution networks (GCNs), which operate on graph data, have attracted a great deal of attention. However, there is no preceding research on the influence functions of GCNs to shed light on the effects of removing training nodes/edges from an input graph. Since the nodes/edges in a graph are interdependent in GCNs, it is challenging to derive influence functions for GCNs. To fill this gap, we started with the simple graph convolution (SGC) model that operates on an attributed graph, and formulated an influence function to approximate the changes of model parameters when a node or an edge is removed from an attributed graph. Moreover, we theoretically analyzed the error bound of the estimated influence of removing an edge. We experimentally validated the accuracy and effectiveness of our influence estimation function. In addition, we showed that the influence function of a SGC model could be used to estimate the impact of removing training nodes/edges on the test performance of the SGC without re-training the model. Finally, we demonstrated how to use influence functions to effectively guide the adversarial attacks on GCNs.
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https://openreview.net/pdf/f5e3b19ac55021309da3afa4dab30f895ed55215.pdf
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SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
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https://openreview.net/forum?id=jh1nCir1R3d
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https://openreview.net/forum?id=jh1nCir1R3d
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Marco Bornstein,Tahseen Rabbani,Evan Z Wang,Amrit Bedi,Furong Huang
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ICLR 2023,Poster
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The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable or untrustworthy central host by utilizing groups of clients to collaboratively train a model via localized training and model/gradient sharing. Most existing decentralized FL algorithms require synchronization of client models where the speed of synchronization depends upon the slowest client. In this work, we propose SWIFT: a novel wait-free decentralized FL algorithm that allows clients to conduct training at their own speed. Theoretically, we prove that SWIFT matches the gold-standard iteration convergence rate $\mathcal{O}(1/\sqrt{T})$ of parallel stochastic gradient descent for convex and non-convex smooth optimization (total iterations $T$). Furthermore, we provide theoretical results for IID and non-IID settings without any bounded-delay assumption for slow clients which is required by other asynchronous decentralized FL algorithms. Although SWIFT achieves the same iteration convergence rate with respect to $T$ as other state-of-the-art (SOTA) parallel stochastic algorithms, it converges faster with respect to runtime due to its wait-free structure. Our experimental results demonstrate that SWIFT's runtime is reduced due to a large reduction in communication time per epoch, which falls by an order of magnitude compared to synchronous counterparts. Furthermore, SWIFT produces loss levels for image classification, over IID and non-IID data settings, upwards of 50\% faster than existing SOTA algorithms.
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https://openreview.net/pdf/8958ed315a36eceffb2712c6e256766b009fdd23.pdf
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Hierarchical Sliced Wasserstein Distance
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https://openreview.net/forum?id=CUOaVn6mYEj
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https://openreview.net/forum?id=CUOaVn6mYEj
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Khai Nguyen,Tongzheng Ren,Huy Nguyen,Litu Rout,Tan Minh Nguyen,Nhat Ho
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ICLR 2023,Poster
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Sliced Wasserstein (SW) distance has been widely used in different application scenarios since it can be scaled to a large number of supports without suffering from the curse of dimensionality. The value of sliced Wasserstein distance is the average of transportation cost between one-dimensional representations (projections) of original measures that are obtained by Radon Transform (RT). Despite its efficiency in the number of supports, estimating the sliced Wasserstein requires a relatively large number of projections in high-dimensional settings. Therefore, for applications where the number of supports is relatively small compared with the dimension, e.g., several deep learning applications where the mini-batch approaches are utilized, the complexities from matrix multiplication of Radon Transform become the main computational bottleneck. To address this issue, we propose to derive projections by linearly and randomly combining a smaller number of projections which are named bottleneck projections. We explain the usage of these projections by introducing Hierarchical Radon Transform (HRT) which is constructed by applying Radon Transform variants recursively. We then formulate the approach into a new metric between measures, named Hierarchical Sliced Wasserstein (HSW) distance. By proving the injectivity of HRT, we derive the metricity of HSW. Moreover, we investigate the theoretical properties of HSW including its connection to SW variants and its computational and sample complexities. Finally, we compare the computational cost and generative quality of HSW with the conventional SW on the task of deep generative modeling using various benchmark datasets including CIFAR10, CelebA, and Tiny ImageNet.
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https://openreview.net/pdf/5503447c4f2c86286908b993e39c387ac34cdc2f.pdf
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Prototypical Calibration for Few-shot Learning of Language Models
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https://openreview.net/forum?id=nUsP9lFADUF
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https://openreview.net/forum?id=nUsP9lFADUF
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Zhixiong Han,Yaru Hao,Li Dong,Yutao Sun,Furu Wei
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ICLR 2023,Poster
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In-context learning of GPT-like models has been recognized as fragile across different hand-crafted templates, and demonstration permutations. In this work, we propose prototypical calibration to adaptively learn a more robust decision boundary for zero- and few-shot classification, instead of greedy decoding. Concretely, our method first adopts Gaussian mixture distribution to estimate the prototypical clusters for all categories. Then we assign each cluster to the corresponding label by solving a weighted bipartite matching problem. Given an example, its prediction is calibrated by the likelihood of prototypical clusters. Experimental results show that prototypical calibration yields a substantial improvement on a diverse set of tasks. Extensive analysis across different scales also indicates that our method calibrates the decision boundary as expected, greatly improving the robustness of GPT to templates, permutations, and class imbalance.
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https://openreview.net/pdf/a3d3c6d2541dcef2a64adf0dc44b317522722d44.pdf
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NERDS: A General Framework to Train Camera Denoisers from Raw-RGB Noisy Image Pairs
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https://openreview.net/forum?id=NO0ThzteQdI
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https://openreview.net/forum?id=NO0ThzteQdI
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Heewon Kim,Kyoung Mu Lee
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ICLR 2023,Poster
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We aim to train accurate denoising networks for smartphone/digital cameras from single noisy images. Downscaling is commonly used as a practical denoiser for low-resolution images. Based on this processing, we found that the pixel variance of the natural images is more robust to downscaling than the pixel variance of the camera noises. Intuitively, downscaling easily removes high-frequency noises than natural textures. To utilize this property, we can adopt noisy/clean image synthesis at low-resolution to train camera denoisers. On this basis, we propose a new solution pipeline -- NERDS that estimates camera noises and synthesizes noisy-clean image pairs from only noisy images. In particular, it first models the noise in raw-sensor images as a Poisson-Gaussian distribution, then estimates the noise parameters using the difference of pixel variances by downscaling. We formulate the noise estimation as a gradient-descent-based optimization problem through a reparametrization trick. We further introduce a new Image Signal Processor (ISP) estimation method that enables denoiser training in a human-readable RGB space by transforming the synthetic raw images to the style of a given RGB noisy image. The noise and ISP estimations utilize rich augmentation to synthesize image pairs for denoiser training. Experiments show that our NERDS can accurately train CNN-based denoisers (e.g., DnCNN, ResNet-style network) outperforming previous noise-synthesis-based and self-supervision-based denoisers in real datasets.
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https://openreview.net/pdf/bdebcf036670b6aad12923bc167527b706b42167.pdf
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Learning Hierarchical Protein Representations via Complete 3D Graph Networks
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https://openreview.net/forum?id=9X-hgLDLYkQ
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https://openreview.net/forum?id=9X-hgLDLYkQ
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Limei Wang,Haoran Liu,Yi Liu,Jerry Kurtin,Shuiwang Ji
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ICLR 2023,Poster
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We consider representation learning for proteins with 3D structures. We build 3D graphs based on protein structures and develop graph networks to learn their representations. Depending on the levels of details that we wish to capture, protein representations can be computed at different levels, \emph{e.g.}, the amino acid, backbone, or all-atom levels. Importantly, there exist hierarchical relations among different levels. In this work, we propose to develop a novel hierarchical graph network, known as ProNet, to capture the relations. Our ProNet is very flexible and can be used to compute protein representations at different levels of granularity. By treating each amino acid as a node in graph modeling as well as harnessing the inherent hierarchies, our ProNet is more effective and efficient than existing methods. We also show that, given a base 3D graph network that is complete, our ProNet representations are also complete at all levels. Experimental results show that ProNet outperforms recent methods on most datasets. In addition, results indicate that different downstream tasks may require representations at different levels. Our code is publicly available as part of the DIG library (\url{https://github.com/divelab/DIG}).
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https://openreview.net/pdf/77f0c38def3be1bf968bafcf011df4cf4570bbfe.pdf
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RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection
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https://openreview.net/forum?id=1UbNwQC89a
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https://openreview.net/forum?id=1UbNwQC89a
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Shancong Mou,Xiaoyi Gu,Meng Cao,Haoping Bai,Ping Huang,Jiulong Shan,Jianjun Shi
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ICLR 2023,Poster
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Generative adversarial networks (GANs), trained on a large-scale image dataset, can be a good approximator of the natural image manifold. GAN-inversion, using a pre-trained generator as a deep generative prior, is a promising tool for image restoration under corruptions. However, the performance of GAN-inversion can be limited by a lack of robustness to unknown gross corruptions, i.e., the restored image might easily deviate from the ground truth. In this paper, we propose a Robust GAN-inversion (RGI) method with a provable robustness guarantee to achieve image restoration under unknown \textit{gross} corruptions, where a small fraction of pixels are completely corrupted. Under mild assumptions, we show that the restored image and the identified corrupted region mask converge asymptotically to the ground truth. Moreover, we extend RGI to Relaxed-RGI (R-RGI) for generator fine-tuning to mitigate the gap between the GAN learned manifold and the true image manifold while avoiding trivial overfitting to the corrupted input image, which further improves the image restoration and corrupted region mask identification performance. The proposed RGI/R-RGI method unifies two important applications with state-of-the-art (SOTA) performance: (i) mask-free semantic inpainting, where the corruptions are unknown missing regions, the restored background can be used to restore the missing content. (ii) unsupervised pixel-wise anomaly detection, where the corruptions are unknown anomalous regions, the retrieved mask can be used as the anomalous region’s segmentation mask.
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https://openreview.net/pdf/a564536c89cace1f99fc67dc6cfe05a26c8c2784.pdf
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Coverage-centric Coreset Selection for High Pruning Rates
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https://openreview.net/forum?id=QwKvL6wC8Yi
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https://openreview.net/forum?id=QwKvL6wC8Yi
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Haizhong Zheng,Rui Liu,Fan Lai,Atul Prakash
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ICLR 2023,Poster
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One-shot coreset selection aims to select a representative subset of the training data, given a pruning rate, that can later be used to train future models while retaining high accuracy. State-of-the-art coreset selection methods pick the highest importance examples based on an importance metric and are found to perform well at low pruning rates. However, at high pruning rates, they suffer from a catastrophic accuracy drop, performing worse than even random sampling. This paper explores the reasons behind this accuracy drop both theoretically and empirically. We first propose a novel metric to measure the coverage of a dataset on a specific distribution by extending the classical geometric set cover problem to a distribution cover problem. This metric helps explain why coresets selected by SOTA methods at high pruning rates perform poorly compared to random sampling because of worse data coverage. We then propose a novel one-shot coreset selection method, Coverage-centric Coreset Selection (CCS), that jointly considers overall data coverage upon a distribution as well as the importance of each example. We evaluate CCS on five datasets and show that, at high pruning rates (e.g., 90%), it achieves significantly better accuracy than previous SOTA methods (e.g., at least 19.56% higher on CIFAR10) as well as random selection (e.g., 7.04% higher on CIFAR10) and comparable accuracy at low pruning rates. We make our code publicly available at https://github.com/haizhongzheng/Coverage-centric-coreset-selection.
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https://openreview.net/pdf/78a8c91e208c106c3b66978468a4eb675ae505c2.pdf
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ILA-DA: Improving Transferability of Intermediate Level Attack with Data Augmentation
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https://openreview.net/forum?id=OM7doLjQbOQ
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https://openreview.net/forum?id=OM7doLjQbOQ
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Chiu Wai Yan,Tsz-Him Cheung,Dit-Yan Yeung
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ICLR 2023,Poster
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Adversarial attack aims to generate deceptive inputs to fool a machine learning model. In deep learning, an adversarial input created for a specific neural network can also trick other neural networks. This intriguing property is known as black-box transferability of adversarial examples. To improve black-box transferability, a previously proposed method called Intermediate Level Attack (ILA) fine-tunes an adversarial example by maximizing its perturbation on an intermediate layer of the source model. Meanwhile, it has been shown that simple image transformations can also enhance attack transferability. Based on these two observations, we propose ILA-DA, which employs three novel augmentation techniques to enhance ILA. Specifically, we propose (1) an automated way to apply effective image transformations, (2) an efficient reverse adversarial update technique, and (3) an attack interpolation method to create more transferable adversarial examples. Shown by extensive experiments, ILA-DA greatly outperforms ILA and other state-of-the-art attacks by a large margin. On ImageNet, we attain an average attack success rate of 84.5%, which is 19.5% better than ILA and 4.7% better than the previous state-of-the-art across nine undefended models. For defended models, ILA-DA also leads existing attacks and provides further gains when incorporated into more advanced attack methods.
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https://openreview.net/pdf/63d0b3e2cfd19e6b2032fba420f695fe23a12397.pdf
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Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning
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https://openreview.net/forum?id=x0BPR9iXc1
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https://openreview.net/forum?id=x0BPR9iXc1
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Zaid Khan,Yun Fu
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ICLR 2023,Poster
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Contrastive vision-language models (e.g. CLIP) are typically created by updating all the parameters of a vision model and language model through contrastive training. Can such models be created by a small number of parameter updates to an already-trained language model and vision model? The literature describes techniques that can create vision-language models by updating a small number of parameters in a language model, but these require already aligned visual representations and are non-contrastive, hence unusable for latency-sensitive applications such as neural search. We explore the feasibility and benefits of parameter-efficient contrastive vision-language alignment through transfer learning: creating a model such as CLIP by minimally updating an already-trained vision and language model. We find that a minimal set of parameter updates ($<$7\%) can achieve the same performance as full-model training, and updating specific components ($<$1\% of parameters) can match 75\% of full-model training. We describe a series of experiments: we show that existing knowledge is conserved more strongly in parameter-efficient training and that parameter-efficient scaling scales with model and dataset size. Where paired-image text data is scarce but strong multilingual language models exist (e.g. low resource languages), parameter-efficient training is even preferable to full-model training. Given a fixed compute budget, parameter-efficient training allows training larger models on the same hardware, achieving equivalent performance in less time. Parameter-efficient training hence constitutes an energy-efficient and effective training strategy for contrastive vision-language models that may be preferable to the full-model training paradigm for common use cases.
Code and weights at https://github.com/codezakh/LilT.
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https://openreview.net/pdf/616f79e13971d683f9f48a16c7436a115b065d7f.pdf
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BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion
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https://openreview.net/forum?id=iP77_axu0h3
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https://openreview.net/forum?id=iP77_axu0h3
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Fu-Yun Wang,Da-Wei Zhou,Liu Liu,Han-Jia Ye,Yatao Bian,De-Chuan Zhan,Peilin Zhao
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ICLR 2023,Poster
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Neural networks suffer from catastrophic forgetting when sequentially learning tasks phase-by-phase, making them inapplicable in dynamically updated systems. Class-incremental learning (CIL) aims to enable neural networks to learn different categories at multi-stages. Recently, dynamic-structure-based CIL methods achieve remarkable performance. However, these methods train all modules in a coupled manner and do not consider possible conflicts among modules, resulting in spoilage of eventual predictions. In this work, we propose a unifying energy-based theory and framework called Bi-Compatible Energy-Based Expansion and Fusion (BEEF) to analyze and achieve the goal of CIL. We demonstrate the possibility of training independent modules in a decoupled manner while achieving bi-directional compatibility among modules through two additionally allocated prototypes, and then integrating them into a unifying classifier with minimal cost. Furthermore, BEEF extends the exemplar-set to a more challenging setting, where exemplars are randomly selected and imbalanced, and maintains its performance when prior methods fail dramatically.
Extensive experiments on three widely used benchmarks: CIFAR-100, ImageNet-100, and ImageNet-1000 demonstrate that BEEF achieves state-of-the-art performance in both the ordinary and challenging CIL settings. The Code is available at https://github.com/G-U-N/ICLR23-BEEF.
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https://openreview.net/pdf/17675705f29fec1ec99d9152ef6c0143641cda49.pdf
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Out-of-distribution Representation Learning for Time Series Classification
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https://openreview.net/forum?id=gUZWOE42l6Q
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https://openreview.net/forum?id=gUZWOE42l6Q
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Wang Lu,Jindong Wang,Xinwei Sun,Yiqiang Chen,Xing Xie
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ICLR 2023,Poster
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Time series classification is an important problem in the real world. Due to its non-stationary property that the distribution changes over time, it remains challenging to build models for generalization to unseen distributions. In this paper, we propose to view time series classification from the distribution perspective. We argue that the temporal complexity of a time series dataset could attribute to unknown latent distributions that need characterize. To this end, we propose DIVERSIFY for out-of-distribution (OOD) representation learning on dynamic distributions of times series. DIVERSIFY takes an iterative process: it first obtains the ‘worst-case’ latent distribution scenario via adversarial training, then reduces the gap between these latent distributions. We then show that such an algorithm is theoretically supported. Extensive experiments are conducted on seven datasets with different OOD settings across gesture recognition, speech commands recognition, wearable stress and affect detection, and sensor-based human activity recognition. Qualitative and quantitative results demonstrate that DIVERSIFY significantly outperforms other baselines and effectively characterizes the latent distributions. Code is available at https://github.com/microsoft/robustlearn.
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https://openreview.net/pdf/d789366a598458e3e64dd117350163e00c1c54d1.pdf
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Schema Inference for Interpretable Image Classification
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https://openreview.net/forum?id=VGI9dSmTgPF
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https://openreview.net/forum?id=VGI9dSmTgPF
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Haofei Zhang,Mengqi Xue,Xiaokang Liu,Kaixuan Chen,Jie Song,Mingli Song
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ICLR 2023,Poster
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In this paper, we study a novel inference paradigm, termed as schema inference, that learns to deductively infer the explainable predictions by rebuilding the prior deep neural network (DNN) forwarding scheme, guided by the prevalent philosophical cognitive concept of schema. We strive to reformulate the conventional model inference pipeline into a graph matching policy that associates the extracted visual concepts of an image with the pre-computed scene impression, by analogy with human reasoning mechanism via impression matching. To this end, we devise an elaborated architecture, termed as SchemaNet, as a dedicated instantiation of the proposed schema inference concept, that models both the visual semantics of input instances and the learned abstract imaginations of target categories as topological relational graphs. Meanwhile, to capture and leverage the compositional contributions of visual semantics in a global view, we also introduce a universal Feat2Graph scheme in SchemaNet to establish the relational graphs that contain abundant interaction information. Both the theoretical analysis and the experimental results on several benchmarks demonstrate that the proposed schema inference achieves encouraging performance and meanwhile yields a clear picture of the deductive process leading to the predictions. Our code is available at https://github.com/zhfeing/SchemaNet-PyTorch.
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https://openreview.net/pdf/321e87c3baaa037520c81e22acdc69f4803572a6.pdf
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Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding
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https://openreview.net/forum?id=XFSCKELP3bp
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https://openreview.net/forum?id=XFSCKELP3bp
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Tianyang Hu,Zhili LIU,Fengwei Zhou,Wenjia Wang,Weiran Huang
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ICLR 2023,Poster
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Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contribute to the theoretical understanding of SSCL and uncover its connection to the classic data visualization method, stochastic neighbor embedding (SNE), whose goal is to preserve pairwise distances. From the perspective of preserving neighboring information, SSCL can be viewed as a special case of SNE with the input space pairwise similarities specified by data augmentation. The established correspondence facilitates deeper theoretical understanding of learned features of SSCL, as well as methodological guidelines for practical improvement. Specifically, through the lens of SNE, we provide novel analysis on domain-agnostic augmentations, implicit bias and robustness of learned features. To illustrate the practical advantage, we demonstrate that the modifications from SNE to $t$-SNE can also be adopted in the SSCL setting, achieving significant improvement in both in-distribution and out-of-distribution generalization.
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https://openreview.net/pdf/700ae8a1919a130b30d9e4f33b1e5929b970e5c7.pdf
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Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting
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https://openreview.net/forum?id=OhUAblg27z
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https://openreview.net/forum?id=OhUAblg27z
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Zhang-Wei Hong,Pulkit Agrawal,Remi Tachet des Combes,Romain Laroche
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ICLR 2023,Poster
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Most offline reinforcement learning (RL) algorithms return a target policy maximizing a trade-off between (1) the expected performance gain over the behavior policy that collected the dataset, and (2) the risk stemming from the out-of-distribution-ness of the induced state-action occupancy. It follows that the performance of the target policy is strongly related to the performance of the behavior policy and, thus, the trajectory return distribution of the dataset. We show that in mixed datasets consisting of mostly low-return trajectories and minor high-return trajectories, state-of-the-art offline RL algorithms are overly restrained by low-return trajectories and fail to exploit high-performing trajectories to the fullest. To overcome this issue, we show that, in deterministic MDPs with stochastic initial states, the dataset sampling can be re-weighted to induce an artificial dataset whose behavior policy has a higher return. This re-weighted sampling strategy may be combined with any offline RL algorithm. We further analyze that the opportunity for performance improvement over the behavior policy correlates with the positive-sided variance of the returns of the trajectories in the dataset. We empirically show that while CQL, IQL, and TD3+BC achieve only a part of this potential policy improvement, these same algorithms combined with our reweighted sampling strategy fully exploit the dataset. Furthermore, we empirically demonstrate that, despite its theoretical limitation, the approach may still be efficient in stochastic environments.
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https://openreview.net/pdf/b83963010af384d9aa84006520ff14a6370f893e.pdf
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Self-Consistency Improves Chain of Thought Reasoning in Language Models
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https://openreview.net/forum?id=1PL1NIMMrw
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https://openreview.net/forum?id=1PL1NIMMrw
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Xuezhi Wang,Jason Wei,Dale Schuurmans,Quoc V Le,Ed H. Chi,Sharan Narang,Aakanksha Chowdhery,Denny Zhou
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ICLR 2023,Poster
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Chain-of-thought prompting combined with pretrained large language models has achieved encouraging results on complex reasoning tasks. In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting. It first samples a diverse set of reasoning paths instead of only taking the greedy one, and then selects the most consistent answer by marginalizing out all possible reasoning paths. Self-consistency leverages the intuition that a complex reasoning problem typically admits multiple different ways of thinking leading to its unique correct answer. Our extensive empirical evaluation shows that self-consistency boosts the performance of chain-of-thought prompting with a striking margin on a range of popular arithmetic and commonsense reasoning benchmarks, including GSM8K (+17.9%), SVAMP (+11.0%), AQuA (+12.2%), StrategyQA (+6.4%) and ARC-challenge (+3.9%).
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https://openreview.net/pdf/9d06013867701125040af03996c3aefddc8d58d1.pdf
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Spiking Convolutional Neural Networks for Text Classification
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https://openreview.net/forum?id=pgU3k7QXuz0
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https://openreview.net/forum?id=pgU3k7QXuz0
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Changze Lv,Jianhan Xu,Xiaoqing Zheng
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ICLR 2023,Poster
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Spiking neural networks (SNNs) offer a promising pathway to implement deep neural networks (DNNs) in a more energy-efficient manner since their neurons are sparsely activated and inferences are event-driven. However, there have been very few works that have demonstrated the efficacy of SNNs in language tasks partially because it is non-trivial to represent words in the forms of spikes and to deal with variable-length texts by SNNs. This work presents a "conversion + fine-tuning'' two-step method for training SNN for text classification and proposes a simple but effective way to encode pre-trained word embeddings as spike trains. We show empirically that after further fine-tuning with surrogate gradients, the converted SNNs achieve comparable results to their DNN counterparts across multiple datasets for Both English and Chinese. We also demonstrate that such SNNs are more robust against adversarial attacks than DNNs.
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https://openreview.net/pdf/745129c55fa65daf1429ecb28da67793a25464a0.pdf
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Distributionally Robust Recourse Action
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https://openreview.net/forum?id=E3ip6qBLF7
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https://openreview.net/forum?id=E3ip6qBLF7
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Duy Nguyen,Ngoc Bui,Viet Anh Nguyen
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ICLR 2023,Poster
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A recourse action aims to explain a particular algorithmic decision by showing one specific way in which the instance could be modified to receive an alternate outcome. Existing recourse generation methods often assume that the machine learning model does not change over time. However, this assumption does not always hold in practice because of data distribution shifts, and in this case, the recourse action may become invalid. To redress this shortcoming, we propose the Distributionally Robust Recourse Action (DiRRAc) framework, which generates a recourse action that has high probability of being valid under a mixture of model shifts. We first formulate the robustified recourse setup as a min-max optimization problem, where the max problem is specified by Gelbrich distance over an ambiguity set around the distribution of model parameters. Then we suggest a projected gradient descent algorithm to find a robust recourse according to the min-max objective. We also show that our DiRRAc framework can be extended to hedge against the misspecification of the mixture weights. Numerical experiments with both synthetic and three real-world datasets demonstrate the benefits of our proposed framework over the state-of-the-art recourse methods, which generate robust recourses.
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https://openreview.net/pdf/2ba3251326914f789659bf5c76f1bc06b8567cb4.pdf
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Write and Paint: Generative Vision-Language Models are Unified Modal Learners
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https://openreview.net/forum?id=HgQR0mXQ1_a
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https://openreview.net/forum?id=HgQR0mXQ1_a
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Shizhe Diao,Wangchunshu Zhou,Xinsong Zhang,Jiawei Wang
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ICLR 2023,Poster
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Recent advances in vision-language pre-training have pushed the state-of-the-art on various vision-language tasks, making machines more capable of multi-modal writing (image-to-text generation) and painting (text-to-image generation). However, few studies investigate if these two essential capabilities can be learned together and boost each other, making a versatile and powerful multi-modal foundation model. In this work, we disclose the potential of symmetric generative vision-language pre-training in learning to write and paint concurrently, and propose a new unified modal model, named DaVinci, trained with prefix language modeling and prefix image modeling, a simple generative self-supervised objective on image-text pairs. Thanks to the proposed prefix multi-modal modeling framework, DaVinci is simple to train, scalable to huge data, adaptable to both writing and painting tasks, and also strong on other vision, text, and multi-modal understanding tasks. DaVinci achieves competitive performance on a wide range of 27 generation/understanding tasks and demonstrates the superiority of combining vision/language generative pre-training. Furthermore, we carefully benchmark the performance of different vision-language pre-training objectives on different scales of pre-training datasets on a heterogeneous and broad distribution coverage. Our results demonstrate the potential of exploiting self-supervision in both language and vision inputs, and establish new, stronger baselines for future comparisons at different data scales. The code and pre-trained models are available at https://github.com/shizhediao/DaVinci.
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https://openreview.net/pdf/782da1a13b9ef3872a63284ecb7f4a21c757982c.pdf
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Progressive Voronoi Diagram Subdivision Enables Accurate Data-free Class-Incremental Learning
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https://openreview.net/forum?id=zJXg_Wmob03
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https://openreview.net/forum?id=zJXg_Wmob03
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Chunwei Ma,Zhanghexuan Ji,Ziyun Huang,Yan Shen,Mingchen Gao,Jinhui Xu
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ICLR 2023,Poster
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Data-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs). In this paper, we present \emph{iVoro}, a novel framework derived from computational geometry. We found Voronoi Diagram (VD), a classical model for space subdivision, is especially powerful for solving the CIL problem, because VD itself can be constructed favorably in an incremental manner -- the newly added sites (classes) will only affect the proximate classes, making the non-contiguous classes hardly forgettable. Furthermore, we bridge DNN and VD using Power Diagram Reduction, and show that the VD structure can be progressively refined along the phases using a divide-and-conquer algorithm. Moreover, our VD construction is not restricted to the deep feature space, but is also applicable to multiple intermediate feature spaces, promoting VD to be multilayer VD that efficiently captures multi-grained features from DNN. Importantly, \emph{iVoro} is also capable of handling uncertainty-aware test-time Voronoi cell assignment and has exhibited high correlations between geometric uncertainty and predictive accuracy (up to ${\sim}0.9$). Putting everything together, \emph{iVoro} achieves up to $25.26\%$, $37.09\%$, and $33.21\%$ improvements on CIFAR-100, TinyImageNet, and ImageNet-Subset, respectively, compared to the state-of-the-art non-exemplar CIL approaches. In conclusion, \emph{iVoro} enables highly accurate, privacy-preserving, and geometrically interpretable CIL that is particularly useful when cross-phase data sharing is forbidden, e.g. in medical applications.
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https://openreview.net/pdf/fa71e6b29715caf412bd9f071aea37172a746fc3.pdf
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Data Valuation Without Training of a Model
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https://openreview.net/forum?id=XIzO8zr-WbM
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https://openreview.net/forum?id=XIzO8zr-WbM
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Ki Nohyun,Hoyong Choi,Hye Won Chung
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ICLR 2023,Poster
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Many recent works on understanding deep learning try to quantify how much individual data instances influence the optimization and generalization of a model. Such attempts reveal characteristics and importance of individual instances, which may provide useful information in diagnosing and improving deep learning. However, most of the existing works on data valuation require actual training of a model, which often demands high-computational cost. In this paper, we provide a training-free data valuation score, called complexity-gap score, which is a data-centric score to quantify the influence of individual instances in generalization of two-layer overparameterized neural networks. The proposed score can quantify irregularity of the instances and measure how much each data instance contributes in the total movement of the network parameters during training. We theoretically analyze and empirically demonstrate the effectiveness of the complexity-gap score in finding `irregular or mislabeled' data instances, and also provide applications of the score in analyzing datasets and diagnosing training dynamics. Our code is publicly available at https://github.com/JJchy/CG_score.
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https://openreview.net/pdf/d2ee5aa2fdaf4401bd826282f3c14838e5770d4b.pdf
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HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing
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https://openreview.net/forum?id=YDJRFWBMNby
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https://openreview.net/forum?id=YDJRFWBMNby
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Tianlong Chen,Chengyue Gong,Daniel Jesus Diaz,Xuxi Chen,Jordan Tyler Wells,qiang liu,Zhangyang Wang,Andrew Ellington,Alex Dimakis,Adam Klivans
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ICLR 2023,Poster
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The molecular basis of protein thermal stability is only partially understood and has major significance for drug and vaccine discovery. The lack of datasets and standardized benchmarks considerably limits learning-based discovery methods. We present \texttt{HotProtein}, a large-scale protein dataset with \textit{growth temperature} annotations of thermostability, containing $182$K amino acid sequences and $3$K folded structures from $230$ different species with a wide temperature range $-20^{\circ}\texttt{C}\sim 120^{\circ}\texttt{C}$. Due to functional domain differences and data scarcity within each species, existing methods fail to generalize well on our dataset. We address this problem through a novel learning framework, consisting of ($1$) Protein structure-aware pre-training (SAP) which leverages 3D information to enhance sequence-based pre-training; ($2$) Factorized sparse tuning (FST) that utilizes low-rank and sparse priors as an implicit regularization, together with feature augmentations. Extensive empirical studies demonstrate that our framework improves thermostability prediction compared to other deep learning models. Finally, we introduce a novel editing algorithm to efficiently generate positive amino acid mutations that improve thermostability. Codes are available in https://github.com/VITA-Group/HotProtein.
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https://openreview.net/pdf/2e8f5a95a911c55a1dbf828fed9a9e23a319b027.pdf
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RPM: Generalizable Multi-Agent Policies for Multi-Agent Reinforcement Learning
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https://openreview.net/forum?id=HnSceSzlfrY
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https://openreview.net/forum?id=HnSceSzlfrY
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Wei Qiu,Xiao Ma,Bo An,Svetlana Obraztsova,Shuicheng YAN,Zhongwen Xu
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ICLR 2023,Poster
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Despite the recent advancement in multi-agent reinforcement learning (MARL), the MARL agents easily overfit the training environment and perform poorly in evaluation scenarios where other agents behave differently. Obtaining generalizable policies for MARL agents is thus necessary but challenging mainly due to complex multi-agent interactions. In this work, we model the MARL problem with Markov Games and propose a simple yet effective method, called ranked policy memory (RPM), i.e., to maintain a look-up memory of policies to achieve good generalizability. The main idea of RPM is to train MARL policies via gathering massive multi-agent interaction data. In particular, we first rank each agent’s policies by its training episode return, i.e., the episode return of each agent in the training environment; we then save the ranked policies in the memory; when an episode starts, each agent can randomly select a policy from the RPM as the behavior policy. Each agent uses the behavior policy to gather multi-agent interaction data for MARL training. This innovative self-play framework guarantees the diversity of multi-agent interaction in the training data. Experimental results on Melting Pot demonstrate that RPM enables MARL agents to interact with unseen agents in multi-agent generalization evaluation scenarios and complete given tasks. It significantly boosts the performance up to 818% on average.
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https://openreview.net/pdf/e0f67e22108de8d6be84c75330ee0de82ed3ae5a.pdf
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Behavior Prior Representation learning for Offline Reinforcement Learning
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https://openreview.net/forum?id=hQ4K9Bf4G2B
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https://openreview.net/forum?id=hQ4K9Bf4G2B
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Hongyu Zang,Xin Li,Jie Yu,Chen Liu,Riashat Islam,Remi Tachet des Combes,Romain Laroche
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ICLR 2023,Poster
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Offline reinforcement learning (RL) struggles in environments with rich and noisy inputs, where the agent only has access to a fixed dataset without environment interactions. Past works have proposed common workarounds based on the pre-training of state representations, followed by policy training. In this work, we introduce a simple, yet effective approach for learning state representations. Our method, Behavior Prior Representation (BPR), learns state representations with an easy-to-integrate objective based on behavior cloning of the dataset: we first learn a state representation by mimicking actions from the dataset, and then train a policy on top of the fixed representation, using any off-the-shelf Offline RL algorithm. Theoretically, we prove that BPR carries out performance guarantees when integrated into algorithms that have either policy improvement guarantees (conservative algorithms) or produce lower bounds of the policy values (pessimistic algorithms). Empirically, we show that BPR combined with existing state-of-the-art Offline RL algorithms leads to significant improvements across several offline control benchmarks. The code is available at \url{https://github.com/bit1029public/offline_bpr}
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https://openreview.net/pdf/96eb2ddb9c4aa37f98b044bc2a20d050badf1645.pdf
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SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency
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https://openreview.net/forum?id=o0LFPcoFKnr
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https://openreview.net/forum?id=o0LFPcoFKnr
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Junfeng Guo,Yiming Li,Xun Chen,Hanqing Guo,Lichao Sun,Cong Liu
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ICLR 2023,Poster
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Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries embed a hidden backdoor trigger during the training process for malicious prediction manipulation. These attacks pose great threats to the applications of DNNs under the real-world machine learning as a service (MLaaS) setting, where the deployed model is fully black-box while the users can only query and obtain its predictions. Currently, there are many existing defenses to reduce backdoor threats. However, almost all of them cannot be adopted in MLaaS scenarios since they require getting access to or even modifying the suspicious models. In this paper, we propose a simple yet effective black-box input-level backdoor detection, called SCALE-UP, which requires only the predicted labels to alleviate this problem. Specifically, we identify and filter malicious testing samples by analyzing their prediction consistency during the pixel-wise amplification process. Our defense is motivated by an intriguing observation (dubbed \emph{scaled prediction consistency}) that the predictions of poisoned samples are significantly more consistent compared to those of benign ones when amplifying all pixel values. Besides, we also provide theoretical foundations to explain this phenomenon. Extensive experiments are conducted on benchmark datasets, verifying the effectiveness and efficiency of our defense and its resistance to potential adaptive attacks. Our codes are available at \url{https://github.com/JunfengGo/SCALE-UP}.
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https://openreview.net/pdf/341ae2d07a7459242b24bb6e6ff7e2aec7a756e1.pdf
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On the Perils of Cascading Robust Classifiers
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https://openreview.net/forum?id=tQG-o3SeipT
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https://openreview.net/forum?id=tQG-o3SeipT
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Ravi Mangal,Zifan Wang,Chi Zhang,Klas Leino,Corina Pasareanu,Matt Fredrikson
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ICLR 2023,Poster
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Ensembling certifiably robust neural networks is a promising approach for improving the \emph{certified robust accuracy} of neural models.
Black-box ensembles that assume only query-access to the constituent models (and their robustness certifiers) during prediction are particularly attractive due to their modular structure. Cascading ensembles are a popular instance of black-box ensembles that appear to improve certified robust accuracies in practice. However, we show that the robustness certifier used by a cascading ensemble is unsound. That is, when a cascading ensemble is certified as locally robust at an input $x$ (with respect to $\epsilon$), there can be inputs $x'$ in the $\epsilon$-ball centered at $x$, such that the cascade's prediction at $x'$ is different from $x$ and thus the ensemble is not locally robust. Our theoretical findings are accompanied by empirical results that further demonstrate this unsoundness. We present a new attack against cascading ensembles and show that: (1) there exists an adversarial input for up to 88\% of the samples where the ensemble claims to be certifiably robust and accurate; and (2) the accuracy of a cascading ensemble under our attack is as low as 11\% when it claims to be certifiably robust and accurate on 97\% of the test set. Our work reveals a critical pitfall of cascading certifiably robust models by showing that the seemingly beneficial strategy of cascading can actually hurt the robustness of the resulting ensemble. Our code is available at https://github.com/TristaChi/ensembleKW.
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https://openreview.net/pdf/7ea45a979586553655c6c9eedc0decdb777aaf1e.pdf
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Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions
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https://openreview.net/forum?id=4D4TSJE6-K
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https://openreview.net/forum?id=4D4TSJE6-K
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Ansong Ni,Jeevana Priya Inala,Chenglong Wang,Alex Polozov,Christopher Meek,Dragomir Radev,Jianfeng Gao
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ICLR 2023,Poster
|
Pretrained language models have shown superior performance on many natural language processing tasks, yet they still struggle at multi-step formal reasoning tasks like grade school math problems. One key challenge of finetuning them to solve such math reasoning problems is that many existing datasets only contain one reference solution for each problem, despite the fact that there are often alternative solutions resembling different reasoning paths to the final answer. This way, the finetuned models are biased towards the limited reference solutions, which limits their generalization to unseen examples. To mitigate this issue, we propose to let the model perform sampling during training and learn from both self-sampled fully-correct solutions, which yield the correct answer upon execution, and partially-correct solutions, whose intermediate state matches an intermediate state of a known correct solution. We show that our use of self-sampled correct and partially-correct solutions can benefit learning and help guide the sampling process, leading to more efficient exploration of the solution space. Additionally, we explore various training objectives to support learning from multiple solutions per example and find they greatly affect the performance. Experiments on two math reasoning datasets show the effectiveness of our method compared to learning from a single reference solution with MLE, where we improve PASS@100 from 35.5% to 44.5% for GSM8K, and 27.6% to 36.2% PASS@80 for MathQA. Such improvements are also consistent across different model sizes.
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https://openreview.net/pdf/cd9398ff5cb3cad0cd4e635371b5bd8915893cd4.pdf
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Adaptive Robust Evidential Optimization For Open Set Detection from Imbalanced Data
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https://openreview.net/forum?id=3yJ-hcJBqe
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https://openreview.net/forum?id=3yJ-hcJBqe
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Hitesh Sapkota,Qi Yu
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ICLR 2023,Poster
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Open set detection (OSD) aims at identifying data samples of an unknown class ($i.e.$, open set) from those of known classes ($i.e.$, closed set) based on a model trained from closed set samples. However, a closed set may involve a highly imbalanced class distribution. Accurately differentiating open set samples and those from a minority class in the closed set poses a fundamental challenge as the model may be equally uncertain when recognizing samples from the minority class. In this paper, we propose Adaptive Robust Evidential Optimization (AREO) that offers a principled way to quantify sample uncertainty through evidential learning while optimally balancing the model training over all classes in the closed set through adaptive distributively robust optimization (DRO). To avoid the model to primarily focus on the most difficult samples by following the standard DRO, adaptive DRO training is performed, which is governed by a novel multi-scheduler learning mechanism to ensure an optimal model training behavior that gives sufficient attention to the difficult samples and the minority class while capable of learning common patterns from the majority classes. Our experimental results on multiple real-world datasets demonstrate that the proposed model outputs uncertainty scores that can clearly separate samples from closed and open sets, respectively, and the detection results outperform the competitive baselines.
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https://openreview.net/pdf/9f55825130626be96ff0171db6bbf752500eeeda.pdf
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Dual Diffusion Implicit Bridges for Image-to-Image Translation
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https://openreview.net/forum?id=5HLoTvVGDe
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https://openreview.net/forum?id=5HLoTvVGDe
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Xuan Su,Jiaming Song,Chenlin Meng,Stefano Ermon
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ICLR 2023,Poster
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Common image-to-image translation methods rely on joint training over data from both source and target domains. The training process requires concurrent access to both datasets, which hinders data separation and privacy protection; and existing models cannot be easily adapted for translation of new domain pairs. We present Dual Diffusion Implicit Bridges (DDIBs), an image translation method based on diffusion models, that circumvents training on domain pairs. Image translation with DDIBs relies on two diffusion models trained independently on each domain, and is a two-step process: DDIBs first obtain latent encodings for source images with the source diffusion model, and then decode such encodings using the target model to construct target images. Both steps are defined via ordinary differential equations (ODEs), thus the process is cycle consistent only up to discretization errors of the ODE solvers. Theoretically, we interpret DDIBs as concatenation of source to latent, and latent to target Schrodinger Bridges, a form of entropy-regularized optimal transport, to explain the efficacy of the method. Experimentally, we apply DDIBs on synthetic and high-resolution image datasets, to demonstrate their utility in a wide variety of translation tasks and their inherent optimal transport properties.
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https://openreview.net/pdf/91f1c96de0279c81fb44166262ba54d69daf0fe4.pdf
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Average Sensitivity of Decision Tree Learning
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https://openreview.net/forum?id=boik01yhssB
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https://openreview.net/forum?id=boik01yhssB
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Satoshi Hara,Yuichi Yoshida
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ICLR 2023,Poster
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A decision tree is a fundamental model used in data mining and machine learning. In practice, the training data used to construct a decision tree may change over time or contain noise, and a drastic change in the learned tree structure owing to such data perturbation is unfavorable. For example, in data mining, a change in the tree implies a change in the extracted knowledge, which raises the question of whether the extracted knowledge is truly reliable or is only a noisy artifact. To alleviate this issue, we design decision tree learning algorithms that are stable against insignificant perturbations in the training data. Specifically, we adopt the notion of average sensitivity as a stability measure, and design an algorithm with low average sensitivity that outputs a decision tree whose accuracy is close to the optimal decision tree. The experimental results on real-world datasets demonstrate that the proposed algorithm enables users to select suitable decision trees considering the trade-off between average sensitivity and accuracy.
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https://openreview.net/pdf/db4880381e5ca7a65fa5ae7f37e99287c8978e9d.pdf
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Stable Target Field for Reduced Variance Score Estimation in Diffusion Models
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https://openreview.net/forum?id=WmIwYTd0YTF
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https://openreview.net/forum?id=WmIwYTd0YTF
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Yilun Xu,Shangyuan Tong,Tommi S. Jaakkola
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ICLR 2023,Poster
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Diffusion models generate samples by reversing a fixed forward diffusion process. Despite already providing impressive empirical results, these diffusion models algorithms can be further improved by reducing the variance of the training targets in their denoising score-matching objective. We argue that the source of such variance lies in the handling of intermediate noise-variance scales, where multiple modes in the data affect the direction of reverse paths. We propose to remedy the problem by incorporating a reference batch which we use to calculate weighted conditional scores as more stable training targets. We show that the procedure indeed helps in the challenging intermediate regime by reducing (the trace of) the covariance of training targets. The new stable targets can be seen as trading bias for reduced variance, where the bias vanishes with increasing reference batch size. Empirically, we show that the new objective improves the image quality, stability, and training speed of various popular diffusion models across datasets with both general ODE and SDE solvers. When used in combination with EDM, our method yields a current SOTA FID of 1.90 with 35 network evaluations on the unconditional CIFAR-10 generation task. The code is available at https://github.com/Newbeeer/stf
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https://openreview.net/pdf/bd21f9db32efdae84c04fffb45dc533162bb1809.pdf
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Continuous pseudo-labeling from the start
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https://openreview.net/forum?id=m3twGT2bAug
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https://openreview.net/forum?id=m3twGT2bAug
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Dan Berrebbi,Ronan Collobert,Samy Bengio,Navdeep Jaitly,Tatiana Likhomanenko
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ICLR 2023,Poster
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Self-training (ST), or pseudo-labeling has sparked significant interest in the automatic speech recognition (ASR) community recently because of its success in harnessing unlabeled data. Unlike prior semi-supervised learning approaches that relied on iteratively regenerating pseudo-labels (PLs) from a trained model and using them to train a new model, recent state-of-the-art methods perform `continuous training' where PLs are generated using a very recent version of the model being trained. Nevertheless, these approaches still rely on bootstrapping the ST using an initial supervised learning phase where the model is trained on labeled data alone. We believe this has the potential for over-fitting to the labeled dataset in low resource settings and that ST from the start of training should reduce over-fitting. In this paper we show how we can do this by dynamically controlling the evolution of PLs during the training process in ASR. To the best of our knowledge, this is the first study that shows the feasibility of generating PLs from the very start of the training. We are able to achieve this using two techniques that avoid instabilities which lead to degenerate models that do not generalize. Firstly, we control the evolution of PLs through a curriculum that uses the online changes in PLs to control the membership of the cache of PLs and improve generalization. Secondly, we find that by sampling transcriptions from the predictive distribution, rather than only using the best transcription, we can stabilize training further. With these techniques, our ST models match prior works without an external language model.
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https://openreview.net/pdf/798c3dbf85d7a7bebf220083f508c53e8569a286.pdf
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GNNDelete: A General Strategy for Unlearning in Graph Neural Networks
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https://openreview.net/forum?id=X9yCkmT5Qrl
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https://openreview.net/forum?id=X9yCkmT5Qrl
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Jiali Cheng,George Dasoulas,Huan He,Chirag Agarwal,Marinka Zitnik
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ICLR 2023,Poster
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Graph unlearning, which involves deleting graph elements such as nodes, node labels, and relationships from a trained graph neural network (GNN) model, is crucial for real-world applications where data elements may become irrelevant, inaccurate, or privacy-sensitive. However, existing methods for graph unlearning either deteriorate model weights shared across all nodes or fail to effectively delete edges due to their strong dependence on local graph neighborhoods. To address these limitations, we introduce GNNDelete, a novel model-agnostic layer-wise operator that optimizes two critical properties, namely, Deleted Edge Consistency and Neighborhood Influence, for graph unlearning. Deleted Edge Consistency ensures that the influence of deleted elements is removed from both model weights and neighboring representations, while Neighborhood Influence guarantees that the remaining model knowledge is preserved after deletion. GNNDelete updates representations to delete nodes and edges from the model while retaining the rest of the learned knowledge. We conduct experiments on seven real-world graphs, showing that GNNDelete outperforms existing approaches by up to 38.8% (AUC) on edge, node, and node feature deletion tasks, and 32.2% on distinguishing deleted edges from non-deleted ones. Additionally, GNNDelete is efficient, taking 12.3x less time and 9.3x less space than retraining GNN from scratch on WordNet18.
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https://openreview.net/pdf/d344e51366b6eeb1347bf96857a1cdeb5ca03e64.pdf
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Meta-Learning in Games
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https://openreview.net/forum?id=uHaWaNhCvZD
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https://openreview.net/forum?id=uHaWaNhCvZD
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Keegan Harris,Ioannis Anagnostides,Gabriele Farina,Mikhail Khodak,Steven Wu,Tuomas Sandholm
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ICLR 2023,Poster
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In the literature on game-theoretic equilibrium finding, focus has mainly been on solving a single game in isolation. In practice, however, strategic interactions—ranging from routing problems to online advertising auctions—evolve dynamically, thereby leading to many similar games to be solved. To address this gap, we introduce meta-learning for equilibrium finding and learning to play games. We establish the first meta-learning guarantees for a variety of fundamental and well-studied games, including two-player zero-sum games, general-sum games, Stackelberg games, and multiple extensions thereof. In particular, we obtain rates of convergence to different game-theoretic equilibria that depend on natural notions of similarity between the sequence of games encountered, while at the same time recovering the known single-game guarantees when the sequence of games is arbitrary. Along the way, we prove a number of new results in the single-game regime through a simple and unified framework, which may be of independent interest. Finally, we evaluate our meta-learning algorithms on endgames faced by the poker agent Libratus against top human professionals. The experiments show that games with varying stack sizes can be solved significantly faster using our meta-learning techniques than by solving them separately, often by an order of magnitude.
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https://openreview.net/pdf/f9d6c07c1a127336efae79b0887b97faf1fdae60.pdf
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DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
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https://openreview.net/forum?id=kKF8_K-mBbS
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https://openreview.net/forum?id=kKF8_K-mBbS
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Gabriele Corso,Hannes Stärk,Bowen Jing,Regina Barzilay,Tommi S. Jaakkola
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ICLR 2023,Poster
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Predicting the binding structure of a small molecule ligand to a protein---a task known as molecular docking---is critical to drug design. Recent deep learning methods that treat docking as a regression problem have decreased runtime compared to traditional search-based methods but have yet to offer substantial improvements in accuracy. We instead frame molecular docking as a generative modeling problem and develop DiffDock, a diffusion generative model over the non-Euclidean manifold of ligand poses. To do so, we map this manifold to the product space of the degrees of freedom (translational, rotational, and torsional) involved in docking and develop an efficient diffusion process on this space. Empirically, DiffDock obtains a 38% top-1 success rate (RMSD<2A) on PDBBind, significantly outperforming the previous state-of-the-art of traditional docking (23%) and deep learning (20%) methods. Moreover, while previous methods are not able to dock on computationally folded structures (maximum accuracy 10.4%), DiffDock maintains significantly higher precision (21.7%). Finally, DiffDock has fast inference times and provides confidence estimates with high selective accuracy.
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https://openreview.net/pdf/1b5dd2372dc371a1494d3dcd7822710879750485.pdf
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Constructive TT-representation of the tensors given as index interaction functions with applications
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https://openreview.net/forum?id=yLzLfM-Esnu
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https://openreview.net/forum?id=yLzLfM-Esnu
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Gleb Ryzhakov,Ivan Oseledets
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ICLR 2023,Poster
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This paper presents a method to build explicit tensor-train (TT) representations. We show that a wide class of tensors can be explicitly represented with sparse TT-cores, obtaining, in many cases, optimal TT-ranks. Numerical experiments show that our method outperforms the existing ones in several practical applications, including game theory problems. Theoretical estimations of the number of operations show that in some problems, such as permanent calculation, our methods are close to the known optimal asymptotics, which are obtained by a completely different type of methods.
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https://openreview.net/pdf/e4da1f4c2b060375df4f5a1488aadc1d7a135c64.pdf
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Sparse tree-based Initialization for Neural Networks
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https://openreview.net/forum?id=78xgBm6ckZr
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https://openreview.net/forum?id=78xgBm6ckZr
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Patrick Lutz,Ludovic Arnould,Claire Boyer,Erwan Scornet
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ICLR 2023,Poster
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Dedicated neural network (NN) architectures have been designed to handle specific data types (such as CNN for images or RNN for text), which ranks them among state-of-the-art methods for dealing with these data. Unfortunately, no architecture has been found for dealing with tabular data yet, for which tree ensemble methods (tree boosting, random forests) usually show the best predictive performances. In this work, we propose a new sparse initialization technique for (potentially deep) multilayer perceptrons (MLP): we first train a tree-based procedure to detect feature interactions and use the resulting information to initialize the network, which is subsequently trained via standard gradient descent (GD) strategies. Numerical experiments on several tabular data sets showthe benefits of this new, simple and easy-to-use method, both in terms of generalization capacity and computation time, compared to default MLP initialization and even to existing complex deep learning solutions. In fact, this wise MLP initialization raises the performances of the resulting NN methods to that of gradient boosting on tabular data. Besides, such initializations are able to preserve the sparsity of weights introduced in the first layers of the network throughout the training, which emphasizes that the first layers act as a sparse feature extractor (like convolutional layers in CNN).
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https://openreview.net/pdf/682d9f075a2e851fb0b72659e84ed891bdbaeefa.pdf
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