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Certified Training: Small Boxes are All You Need
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https://openreview.net/forum?id=7oFuxtJtUMH
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https://openreview.net/forum?id=7oFuxtJtUMH
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Mark Niklas Mueller,Franziska Eckert,Marc Fischer,Martin Vechev
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ICLR 2023,Top 25%
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To obtain, deterministic guarantees of adversarial robustness, specialized training methods are used. We propose, SABR, a novel such certified training method, based on the key insight that propagating interval bounds for a small but carefully selected subset of the adversarial input region is sufficient to approximate the worst-case loss over the whole region while significantly reducing approximation errors. We show in an extensive empirical evaluation that SABR outperforms existing certified defenses in terms of both standard and certifiable accuracies across perturbation magnitudes and datasets, pointing to a new class of certified training methods promising to alleviate the robustness-accuracy trade-off.
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https://openreview.net/pdf/e61a2e061488e943012e445b9549adde476fd159.pdf
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Multi-Objective Online Learning
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https://openreview.net/forum?id=dKkMnCWfVmm
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https://openreview.net/forum?id=dKkMnCWfVmm
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Jiyan Jiang,Wenpeng Zhang,Shiji Zhou,Lihong Gu,Xiaodong Zeng,Wenwu Zhu
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ICLR 2023,Top 25%
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This paper presents a systematic study of multi-objective online learning. We first formulate the framework of Multi-Objective Online Convex Optimization, which encompasses a novel multi-objective regret. This regret is built upon a sequence-wise extension of the commonly used discrepancy metric Pareto suboptimality gap in zero-order multi-objective bandits. We then derive an equivalent form of the regret, making it amenable to be optimized via first-order iterative methods. To motivate the algorithm design, we give an explicit example in which equipping OMD with the vanilla min-norm solver for gradient composition will incur a linear regret, which shows that merely regularizing the iterates, as in single-objective online learning, is not enough to guarantee sublinear regrets in the multi-objective setting. To resolve this issue, we propose a novel min-regularized-norm solver that regularizes the composite weights. Combining min-regularized-norm with OMD results in the Doubly Regularized Online Mirror Multiple Descent algorithm. We further derive the multi-objective regret bound for the proposed algorithm, which matches the optimal bound in the single-objective setting. Extensive experiments on several real-world datasets verify the effectiveness of the proposed algorithm.
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https://openreview.net/pdf/e427cee744afa5b7154df61d9ed0fad7ea26144a.pdf
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Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning
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https://openreview.net/forum?id=3ULaIHxn9u7
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https://openreview.net/forum?id=3ULaIHxn9u7
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Xin-Qiang Cai,Yao-Xiang Ding,Zixuan Chen,Yuan Jiang,Masashi Sugiyama,Zhi-Hua Zhou
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ICLR 2023,Top 25%
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In many real-world imitation learning tasks, the demonstrator and the learner have to act under different observation spaces. This situation brings significant obstacles to existing imitation learning approaches, since most of them learn policies under homogeneous observation spaces. On the other hand, previous studies under different observation spaces have strong assumptions that these two observation spaces coexist during the entire learning process. However, in reality, the observation coexistence will be limited due to the high cost of acquiring expert observations. In this work, we study this challenging problem with limited observation coexistence under heterogeneous observations: Heterogeneously Observable Imitation Learning (HOIL). We identify two underlying issues in HOIL: the dynamics mismatch and the support mismatch, and further propose the Importance Weighting with REjection (IWRE) algorithm based on importance weighting and learning with rejection to solve HOIL problems. Experimental results show that IWRE can solve various HOIL tasks, including the challenging tasks of transforming the vision-based demonstrations to random access memory (RAM)-based policies in the Atari domain, even with limited visual observations.
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https://openreview.net/pdf/38e823cd3fba73660cf9a28932a2ba5cad391a39.pdf
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A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias
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https://openreview.net/forum?id=wkg_b4-IwTZ
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https://openreview.net/forum?id=wkg_b4-IwTZ
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Puja Trivedi,Danai Koutra,Jayaraman J. Thiagarajan
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ICLR 2023,Top 25%
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Advances in the expressivity of pretrained models have increased interest in the design of adaptation protocols which enable safe and effective transfer learning. Going beyond conventional linear probing (LP) and fine tuning (FT) strategies, protocols that can effectively control feature distortion, i.e., the failure to update features orthogonal to the in-distribution, have been found to achieve improved out-of-distribution generalization (OOD). In order to limit this distortion, the LP+FT protocol, which first learns a linear probe and then uses this initialization for subsequent FT, was proposed. However, in this paper, we find when adaptation protocols (LP, FT, LP+FT) are also evaluated on a variety of safety objectives (e.g., calibration, robustness, etc.), a complementary perspective to feature distortion is helpful to explain protocol behavior. To this end, we study the susceptibility of protocols to simplicity bias (SB), i.e. the well-known propensity of deep neural networks to rely upon simple features, as SB has recently been shown to underlie several problems in robust generalization. Using a synthetic dataset, we demonstrate the susceptibility of existing protocols to SB. Given the strong effectiveness of LP+FT, we then propose modified linear probes that help mitigate SB, and lead to better initializations for subsequent FT. We verify the effectiveness of the proposed LP+FT variants for decreasing SB in a controlled setting, and their ability to improve OOD generalization and safety on three adaptation datasets.
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https://openreview.net/pdf/a96c8869749346661838b9c685006ca0e44e9011.pdf
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Understanding and Adopting Rational Behavior by Bellman Score Estimation
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https://openreview.net/forum?id=WzGdBqcBicl
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https://openreview.net/forum?id=WzGdBqcBicl
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Kuno Kim,Stefano Ermon
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ICLR 2023,Top 25%
|
We are interested in solving a class of problems that seek to understand and adopt rational behavior from demonstrations. We may broadly classify these problems into four categories of reward identification, counterfactual analysis, behavior imitation, and behavior transfer. In this work, we make a key observation that knowing how changes in the underlying rewards affect the optimal behavior allows one to solve a variety of aforementioned problems. To a local approximation, this quantity is precisely captured by what we term the Bellman score, i.e gradient of log probabilities of the optimal policy with respect to the reward. We introduce the Bellman score operator which provably converges to the gradient of the infinite-horizon optimal Q-values with respect to the reward which can then be used to directly estimate the score. Guided by our theory, we derive a practical score-learning algorithm which can be used for score estimation in high-dimensional state-actions spaces. We show that score-learning can be used to reliably identify rewards, perform counterfactual predictions, achieve state-of-the-art behavior imitation, and transfer policies across environments.
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https://openreview.net/pdf/88eb87ddbc76eb74567b04754a1b2ffc9ad31fbf.pdf
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STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables
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https://openreview.net/forum?id=_xlsjehDvlY
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https://openreview.net/forum?id=_xlsjehDvlY
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Jaehyun Nam,Jihoon Tack,Kyungmin Lee,Hankook Lee,Jinwoo Shin
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ICLR 2023,Top 25%
|
Learning with few labeled tabular samples is often an essential requirement for industrial machine learning applications as varieties of tabular data suffer from high annotation costs or have difficulties in collecting new samples for novel tasks. Despite the utter importance, such a problem is quite under-explored in the field of tabular learning, and existing few-shot learning schemes from other domains are not straightforward to apply, mainly due to the heterogeneous characteristics of tabular data. In this paper, we propose a simple yet effective framework for few-shot semi-supervised tabular learning, coined Self-generated Tasks from UNlabeled Tables (STUNT). Our key idea is to self-generate diverse few-shot tasks by treating randomly chosen columns as a target label. We then employ a meta-learning scheme to learn generalizable knowledge with the constructed tasks. Moreover, we introduce an unsupervised validation scheme for hyperparameter search (and early stopping) by generating a pseudo-validation set using STUNT from unlabeled data. Our experimental results demonstrate that our simple framework brings significant performance gain under various tabular few-shot learning benchmarks, compared to prior semi- and self-supervised baselines. Code is available at https://github.com/jaehyun513/STUNT.
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https://openreview.net/pdf/119c2e47ecfab89ce208c770994cdb25ae1c39c8.pdf
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Ask Me Anything: A simple strategy for prompting language models
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https://openreview.net/forum?id=bhUPJnS2g0X
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https://openreview.net/forum?id=bhUPJnS2g0X
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Simran Arora,Avanika Narayan,Mayee F Chen,Laurel Orr,Neel Guha,Kush Bhatia,Ines Chami,Christopher Re
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ICLR 2023,Top 25%
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Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training. Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly crafted "perfect prompt" for a task. To mitigate the high degree of effort, we instead ask whether collecting multiple decent, yet imperfect, prompts and aggregating them can lead to a high quality prompting strategy. Our observations motivate our proposed method, Ask Me Anything (AMA). We first develop an understanding of the effective prompt formats, finding question-answering (QA) prompts, which encourage open-ended generation ("Who went to the park?") tend to outperform those that restrict the model outputs ("John went to the park. True or False?"). AMA recursively uses the LLM to transform task inputs to the effective QA format. AM generates multiple questions per input and applies these prompts to collect several noisy "votes" for the input's true label. We find the prompts have varying accuracies and dependencies and thus propose to use weak supervision, a procedure for combining the noisy predictions, to produce the final predictions. We evaluate AMA across open-source model families (EleutherAI, BLOOM, OPT, and T0) and sizes (125M-175B parameters), demonstrating an average performance lift of 10.2\% over the few-shot baseline. This simple strategy enables the open-source GPT-J-6B model to match and exceed the performance of few-shot GPT3-175B on 15 of 20 popular benchmarks. Averaged across these tasks, the GPT-J-6B model outperforms few-shot GPT3-175B. We release our code here: https://github.com/HazyResearch/ama_prompting.
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https://openreview.net/pdf/5b1bcdac167fa4b294480f303ac3722afa8a9aac.pdf
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On Representing Linear Programs by Graph Neural Networks
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https://openreview.net/forum?id=cP2QVK-uygd
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https://openreview.net/forum?id=cP2QVK-uygd
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Ziang Chen,Jialin Liu,Xinshang Wang,Wotao Yin
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ICLR 2023,Top 25%
|
Learning to optimize is a rapidly growing area that aims to solve optimization problems or improve existing optimization algorithms using machine learning (ML). In particular, the graph neural network (GNN) is considered a suitable ML model for optimization problems whose variables and constraints are permutation--invariant, for example, the linear program (LP). While the literature has reported encouraging numerical results, this paper establishes the theoretical foundation of applying GNNs to solving LPs. Given any size limit of LPs, we construct a GNN that maps different LPs to different outputs. We show that properly built GNNs can reliably predict feasibility, boundedness, and an optimal solution for each LP in a broad class. Our proofs are based upon the recently--discovered connections between the Weisfeiler--Lehman isomorphism test and the GNN. To validate our results, we train a simple GNN and present its accuracy in mapping LPs to their feasibilities and solutions.
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https://openreview.net/pdf/155020e920d47414c7089209e49eaadf7b34a960.pdf
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Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel
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https://openreview.net/forum?id=VZ5EaTI6dqa
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https://openreview.net/forum?id=VZ5EaTI6dqa
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SungYub Kim,Sihwan Park,Kyung-Su Kim,Eunho Yang
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ICLR 2023,Top 25%
|
Studying the loss landscapes of neural networks is critical to identifying generalizations and avoiding overconfident predictions. Flatness, which measures the perturbation resilience of pre-trained parameters for loss values, is widely acknowledged as an essential predictor of generalization. While the concept of flatness has been formalized as a PAC-Bayes bound, it has been observed that the generalization bounds can vary arbitrarily depending on the scale of the model parameters. Despite previous attempts to address this issue, generalization bounds remain vulnerable to function-preserving scaling transformations or are limited to impractical network structures. In this paper, we introduce new PAC-Bayes prior and posterior distributions invariant to scaling transformations, achieved through the \textit{decomposition of perturbations into scale and connectivity components}. In this way, this approach expands the range of networks to which the resulting generalization bound can be applied, including those with practical transformations such as weight decay with batch normalization. Moreover, we demonstrate that scale-dependency issues of flatness can adversely affect the uncertainty calibration of Laplace approximation, and we propose a solution using our invariant posterior. Our proposed invariant posterior allows for effective measurement of flatness and calibration with low complexity while remaining invariant to practical parameter transformations, also applying it as a reliable predictor of neural network generalization.
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https://openreview.net/pdf/a23231458e686af85e35285f01c8e2aa3ee3cf3b.pdf
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Minimalistic Unsupervised Representation Learning with the Sparse Manifold Transform
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https://openreview.net/forum?id=nN_nBVKAhhD
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https://openreview.net/forum?id=nN_nBVKAhhD
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Yubei Chen,Zeyu Yun,Yi Ma,Bruno Olshausen,Yann LeCun
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ICLR 2023,Top 25%
|
We describe a minimalistic and interpretable method for unsupervised representation learning that does not require data augmentation, hyperparameter tuning, or other engineering designs, but nonetheless achieves performance close to the state-of-the-art (SOTA) SSL methods. Our approach leverages the sparse manifold transform, which unifies sparse coding, manifold learning, and slow feature analysis. With a one-layer deterministic (one training epoch) sparse manifold transform, it is possible to achieve $99.3\%$ KNN top-1 accuracy on MNIST, $81.1\%$ KNN top-1 accuracy on CIFAR-10, and $53.2\%$ on CIFAR-100. With simple gray-scale augmentation, the model achieves $83.2\%$ KNN top-1 accuracy on CIFAR-10 and $57\%$ on CIFAR-100. These results significantly close the gap between simplistic ``white-box'' methods and SOTA methods. We also provide visualization to illustrate how an unsupervised representation transform is formed. The proposed method is closely connected to latent-embedding self-supervised methods and can be treated as the simplest form of VICReg. Though a small performance gap remains between our simple constructive model and SOTA methods, the evidence points to this as a promising direction for achieving a principled and white-box approach to unsupervised representation learning, which has potential to significantly improve learning efficiency.
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https://openreview.net/pdf/ad011e948fd7b2e72ff6dd869898255569519076.pdf
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GEASS: Neural causal feature selection for high-dimensional biological data
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https://openreview.net/forum?id=aKcS3xojnwY
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https://openreview.net/forum?id=aKcS3xojnwY
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Mingze Dong,Yuval Kluger
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ICLR 2023,Top 25%
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Identifying nonlinear causal relationships in high-dimensional biological data is an important task. However, current neural network based causality detection approaches for such data suffer from poor interpretability and cannot scale well to the high dimensional regime. Here we present GEASS (Granger fEAture Selection of Spatiotemporal data), which identifies sparse Granger causality mechanisms of high dimensional spatiotemporal data by a single neural network. GEASS maximizes sparsity-regularized modified transfer entropy with a theoretical guarantee of recovering features with spatial/temporal Granger causal relationships. The sparsity regularization is achieved by a novel combinatorial stochastic gate layer to select sparse non-overlapping feature subsets. We demonstrate the efficacy of GEASS in several synthetic datasets and real biological data from single-cell RNA sequencing and spatial transcriptomics.
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https://openreview.net/pdf/cae8ae2947fab56338cdfefd11f686b7a431f9f7.pdf
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SmartFRZ: An Efficient Training Framework using Attention-Based Layer Freezing
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https://openreview.net/forum?id=i9UlAr1T_xl
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https://openreview.net/forum?id=i9UlAr1T_xl
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Sheng Li,Geng Yuan,Yue Dai,Youtao Zhang,Yanzhi Wang,Xulong Tang
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ICLR 2023,Top 25%
|
There has been a proliferation of artificial intelligence applications, where model training is key to promising high-quality services for these applications. However, the model training process is both time-intensive and energy-intensive, inevitably affecting the user's demand for application efficiency. Layer freezing, an efficient model training technique, has been proposed to improve training efficiency. Although existing layer freezing methods demonstrate the great potential to reduce model training costs, they still remain shortcomings such as lacking generalizability and compromised accuracy. For instance, existing layer freezing methods either require the freeze configurations to be manually defined before training, which does not apply to different networks, or use heuristic freezing criteria that is hard to guarantee decent accuracy in different scenarios. Therefore, there lacks a generic and smart layer freezing method that can automatically perform ``in-situation'' layer freezing for different networks during training processes. To this end, we propose a generic and efficient training framework (SmartFRZ). The core proposed technique in SmartFRZ is attention-guided layer freezing, which can automatically select the appropriate layers to freeze without compromising accuracy. Experimental results show that SmartFRZ effectively reduces the amount of computation in training and achieves significant training acceleration, and outperforms the state-of-the-art layer freezing approaches.
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https://openreview.net/pdf/df204364dd4dd09467e128971f66612149d1171b.pdf
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The In-Sample Softmax for Offline Reinforcement Learning
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https://openreview.net/forum?id=u-RuvyDYqCM
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https://openreview.net/forum?id=u-RuvyDYqCM
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Chenjun Xiao,Han Wang,Yangchen Pan,Adam White,Martha White
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ICLR 2023,Top 25%
|
Reinforcement learning (RL) agents can leverage batches of previously collected data to extract a reasonable control policy. An emerging issue in this offline RL setting, however, is that the bootstrapping update underlying many of our methods suffers from insufficient action-coverage: standard max operator may select a maximal action that has not been seen in the dataset. Bootstrapping from these inaccurate values can lead to overestimation and even divergence. There are a growing number of methods that attempt to approximate an in-sample max, that only uses actions well-covered by the dataset. We highlight a simple fact: it is more straightforward to approximate an in-sample softmax using only actions in the dataset. We show that policy iteration based on the in-sample softmax converges, and that for decreasing temperatures it approaches the in-sample max. We derive an In-Sample Actor-Critic (AC), using this in-sample softmax, and show that it is consistently better or comparable to existing offline RL methods, and is also well-suited to fine-tuning. We release the code at github.com/hwang-ua/inac_pytorch.
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https://openreview.net/pdf/69f475d9352b20ebc3fc03da590f54192f7856ec.pdf
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Unsupervised Meta-learning via Few-shot Pseudo-supervised Contrastive Learning
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https://openreview.net/forum?id=TdTGGj7fYYJ
|
https://openreview.net/forum?id=TdTGGj7fYYJ
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Huiwon Jang,Hankook Lee,Jinwoo Shin
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ICLR 2023,Top 25%
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Unsupervised meta-learning aims to learn generalizable knowledge across a distribution of tasks constructed from unlabeled data. Here, the main challenge is how to construct diverse tasks for meta-learning without label information; recent works have proposed to create, e.g., pseudo-labeling via pretrained representations or creating synthetic samples via generative models. However, such a task construction strategy is fundamentally limited due to heavy reliance on the immutable pseudo-labels during meta-learning and the quality of the representations or the generated samples. To overcome the limitations, we propose a simple yet effective unsupervised meta-learning framework, coined Pseudo-supervised Contrast (PsCo), for few-shot classification. We are inspired by the recent self-supervised learning literature; PsCo utilizes a momentum network and a queue of previous batches to improve pseudo-labeling and construct diverse tasks in a progressive manner. Our extensive experiments demonstrate that PsCo outperforms existing unsupervised meta-learning methods under various in-domain and cross-domain few-shot classification benchmarks. We also validate that PsCo is easily scalable to a large-scale benchmark, while recent prior-art meta-schemes are not.
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https://openreview.net/pdf/65c63b72201856c7d08ce81fba8f12b50947aa77.pdf
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Guiding Energy-based Models via Contrastive Latent Variables
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https://openreview.net/forum?id=CZmHHj9MgkP
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https://openreview.net/forum?id=CZmHHj9MgkP
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Hankook Lee,Jongheon Jeong,Sejun Park,Jinwoo Shin
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ICLR 2023,Top 25%
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An energy-based model (EBM) is a popular generative framework that offers both explicit density and architectural flexibility, but training them is difficult since it is often unstable and time-consuming. In recent years, various training techniques have been developed, e.g., better divergence measures or stabilization in MCMC sampling, but there often exists a large gap between EBMs and other generative frameworks like GANs in terms of generation quality. In this paper, we propose a novel and effective framework for improving EBMs via contrastive representation learning (CRL). To be specific, we consider representations learned by contrastive methods as the true underlying latent variable. This contrastive latent variable could guide EBMs to understand the data structure better, so it can improve and accelerate EBM training significantly. To enable the joint training of EBM and CRL, we also design a new class of latent-variable EBMs for learning the joint density of data and the contrastive latent variable. Our experimental results demonstrate that our scheme achieves lower FID scores, compared to prior-art EBM methods (e.g., additionally using variational autoencoders or diffusion techniques), even with significantly faster and more memory-efficient training. We also show conditional and compositional generation abilities of our latent-variable EBMs as their additional benefits, even without explicit conditional training. The code is available at https://github.com/hankook/CLEL.
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https://openreview.net/pdf/9c51d101c5d336bf5bc034b2876d79796069ac59.pdf
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Implicit regularization in Heavy-ball momentum accelerated stochastic gradient descent
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https://openreview.net/forum?id=ZzdBhtEH9yB
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https://openreview.net/forum?id=ZzdBhtEH9yB
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Avrajit Ghosh,He Lyu,Xitong Zhang,Rongrong Wang
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ICLR 2023,Top 25%
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It is well known that the finite step-size ($h$) in Gradient descent (GD) implicitly regularizes solutions to flatter minimas. A natural question to ask is \textit{Does the momentum parameter $\beta$ (say) play a role in implicit regularization in Heavy-ball (H.B) momentum accelerated gradient descent (GD+M)?}. To answer this question, first, we show that the trajectory traced by discrete H.B momentum update (GD+M) is $O(h^2)$ close to a continuous trajectory induced by a modified loss, which consists of an original loss and an implicit regularizer. This implicit regularizer for (GD+M) is indeed stronger than that of (GD) by factor of $(\frac{1+\beta}{1-\beta})$, thus explaining why (GD+M) shows better generalization performance and higher test accuracy than (GD). Furthermore, we extend our analysis to stochastic version of gradient descent with momentum (SGD+M) and propose a deterministic continuous trajectory that is $O(h^2)$ close to the discrete update of (SGD+M) in a strong approximation sense. We explore the implicit regularization in (SGD+M) and (GD+M) through a series of experiments validating our theory.
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https://openreview.net/pdf/dbc1161118a53cfa8a20ab379843b210af71728b.pdf
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Real-time variational method for learning neural trajectory and its dynamics
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https://openreview.net/forum?id=M_MvkWgQSt
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https://openreview.net/forum?id=M_MvkWgQSt
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Matthew Dowling,Yuan Zhao,Il Memming Park
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ICLR 2023,Top 25%
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Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation. This has fostered the development of powerful offline algorithms for extracting latent neural trajectories from neural recordings. However, despite the potential of real-time alternatives to give immediate feedback to experimentalists, and enhance experimental design, they have received markedly less attention. In this work, we introduce the exponential family variational Kalman filter (eVKF), an online recursive Bayesian method aimed at inferring latent trajectories while simultaneously learning the dynamical system generating them. eVKF works for arbitrary likelihoods and utilizes the constant base measure exponential family to model the latent state stochasticity. We derive a closed-form variational analog to the predict step of the Kalman filter which leads to a provably tighter bound on the ELBO compared to another online variational method. We validate our method on synthetic and real-world data, and, notably, show that it achieves competitive performance.
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https://openreview.net/pdf/f2a3ae5af4f08eb6ca19ee6d8642f0023b244943.pdf
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Energy-Inspired Self-Supervised Pretraining for Vision Models
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https://openreview.net/forum?id=ZMz-sW6gCLF
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https://openreview.net/forum?id=ZMz-sW6gCLF
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Ze Wang,Jiang Wang,Zicheng Liu,Qiang Qiu
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ICLR 2023,Top 25%
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Motivated by the fact that forward and backward passes of a deep network naturally form symmetric mappings between input and output representations, we introduce a simple yet effective self-supervised vision model pretraining framework inspired by energy-based models (EBMs). In the proposed framework, we model energy estimation and data restoration as the forward and backward passes of a single network without any auxiliary components, e.g., an extra decoder. For the forward pass, we fit a network to an energy function that assigns low energy scores to samples that belong to an unlabeled dataset, and high energy otherwise. For the backward pass, we restore data from corrupted versions iteratively using gradient-based optimization along the direction of energy minimization. In this way, we naturally fold the encoder-decoder architecture widely used in masked image modeling into the forward and backward passes of a single vision model. Our framework accepts a wide range of pretext tasks with different data corruption methods, and permits models to be pretrained from masked image modeling, patch sorting, and image restoration, including super-resolution, denoising, and colorization. We support our findings with extensive experiments, and show the proposed method delivers comparable and even better performance with remarkably fewer epochs of training compared to the state-of-the-art self-supervised vision model pretraining methods. Our findings shed light on further exploring self-supervised vision model pretraining and pretext tasks beyond masked image modeling.
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https://openreview.net/pdf/36e3907af9186c722c512ea75c280aaae585101e.pdf
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Binding Language Models in Symbolic Languages
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https://openreview.net/forum?id=lH1PV42cbF
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https://openreview.net/forum?id=lH1PV42cbF
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Zhoujun Cheng,Tianbao Xie,Peng Shi,Chengzu Li,Rahul Nadkarni,Yushi Hu,Caiming Xiong,Dragomir Radev,Mari Ostendorf,Luke Zettlemoyer,Noah A. Smith,Tao Yu
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ICLR 2023,Top 25%
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Though end-to-end neural approaches have recently been dominating NLP tasks in both performance and ease-of-use, they lack interpretability and robustness. We propose Binder, a training-free neural-symbolic framework that maps the task input to a program, which (1) allows binding a unified API of language model (LM) functionalities to a programming language (e.g., SQL, Python) to extend its grammar coverage and thus tackle more diverse questions, (2) adopts an LM as both the program parser and the underlying model called by the API during execution, and (3) requires only a few in-context exemplar annotations. Specifically, we employ GPT-3 Codex as the LM. In the parsing stage, with only a few in-context exemplars, Codex is able to identify the part of the task input that cannot be answerable by the original programming language, correctly generate API calls to prompt Codex to solve the unanswerable part, and identify where to place the API calls while being compatible with the original grammar. In the execution stage, Codex can perform versatile functionalities (e.g., commonsense QA, information extraction) given proper prompts in the API calls. Binder achieves state-of-the-art results on WikiTableQuestions and TabFact datasets, with explicit output programs that benefit human debugging. Note that previous best systems are all finetuned on tens of thousands of task-specific samples, while Binder only uses dozens of annotations as in-context exemplars without any training. Our code is available at anonymized.
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https://openreview.net/pdf/d226e827fb59bcd4253c7eb8ce07d339ef5d519d.pdf
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Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems
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https://openreview.net/forum?id=Z4s73sJYQM
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https://openreview.net/forum?id=Z4s73sJYQM
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Zhong Yi Wan,Leonardo Zepeda-Nunez,Anudhyan Boral,Fei Sha
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ICLR 2023,Top 25%
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We present a data-driven, space-time continuous framework to learn surrogate models for complex physical systems described by advection-dominated partial differential equations. Those systems have slow-decaying Kolmogorov n-width that hinders standard methods, including reduced order modeling, from producing high-fidelity simulations at low cost. In this work, we construct hypernetwork-based latent dynamical models directly on the parameter space of a compact representation network. We leverage the expressive power of the network and a specially designed consistency-inducing regularization to obtain latent trajectories that are both low-dimensional and smooth. These properties render our surrogate models highly efficient at inference time. We show the efficacy of our framework by learning models that generate accurate multi-step rollout predictions at much faster inference speed compared to competitors, for several challenging examples.
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https://openreview.net/pdf/fc201e7d56b972a927ba9abff6846d2e611de461.pdf
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BC-IRL: Learning Generalizable Reward Functions from Demonstrations
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https://openreview.net/forum?id=Ovnwe_sDQW
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https://openreview.net/forum?id=Ovnwe_sDQW
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Andrew Szot,Amy Zhang,Dhruv Batra,Zsolt Kira,Franziska Meier
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ICLR 2023,Top 25%
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How well do reward functions learned with inverse reinforcement learning (IRL) generalize? We illustrate that state-of-the-art IRL algorithms, which maximize a maximum-entropy objective, learn rewards that overfit to the demonstrations. Such rewards struggle to provide meaningful rewards for states not covered by the demonstrations, a major detriment when using the reward to learn policies in new situations. We introduce BC-IRL a new inverse reinforcement learning method that learns reward functions that generalize better when compared to maximum-entropy IRL approaches. In contrast to the MaxEnt framework, which learns to maximize rewards around demonstrations, BC-IRL updates reward parameters such that the policy trained with the new reward matches the expert demonstrations better. We show that BC-IRL learns rewards that generalize better on an illustrative simple task and two continuous robotic control tasks, achieving over twice the success rate of baselines in challenging generalization settings.
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https://openreview.net/pdf/214dd3d4f346964ae17621ab8b33fe8cd5a4a444.pdf
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Phase2vec: dynamical systems embedding with a physics-informed convolutional network
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https://openreview.net/forum?id=z9C5dGip90
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https://openreview.net/forum?id=z9C5dGip90
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Matt Ricci,Noa Moriel,Zoe Piran,Mor Nitzan
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ICLR 2023,Top 25%
|
Dynamical systems are found in innumerable forms across the physical and biological sciences, yet all these systems fall naturally into equivalence classes: conservative or dissipative, stable or unstable, compressible or incompressible. Predicting these classes from data remains an essential open challenge in computational physics on which existing time-series classification methods struggle. Here, we propose, phase2vec, an embedding method that learns high-quality, physically-meaningful representations of low-dimensional dynamical systems without supervision. Our embeddings are produced by a convolutional backbone that extracts geometric features from flow data and minimizes a physically-informed vector field reconstruction loss. The trained architecture can not only predict the equations of unseen data, but also produces embeddings that encode meaningful physical properties of input data (e.g. stability of fixed points, conservation of energy, and the incompressibility of flows) more faithfully than standard blackbox classifiers and state-of-the-art time series classification techniques. We additionally apply our embeddings to the analysis of meteorological data, showing we can detect climatically meaningful features. Collectively, our results demonstrate the viability of embedding approaches for the discovery of dynamical features in physical systems.
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https://openreview.net/pdf/be58512bd70dfcd1702f2a09b66d60dfce307f20.pdf
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gDDIM: Generalized denoising diffusion implicit models
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https://openreview.net/forum?id=1hKE9qjvz-
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https://openreview.net/forum?id=1hKE9qjvz-
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Qinsheng Zhang,Molei Tao,Yongxin Chen
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ICLR 2023,Top 25%
|
Our goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models~(DMs) besides isotropic diffusions. Instead of constructing a non-Markov noising process as in the original DDIM, we examine the mechanism of DDIM from a numerical perspective. We discover that the DDIM can be obtained by using some specific approximations of the score when solving the corresponding stochastic differential equation. We present an interpretation of the accelerating effects of DDIM that also explains the advantages of a deterministic sampling scheme over the stochastic one for fast sampling. Building on this insight, we extend DDIM to general DMs, coined generalized DDIM (gDDIM), with a small but delicate modification in parameterizing the score network. We validate gDDIM in two non-isotropic DMs: Blurring diffusion model (BDM) and Critically-damped Langevin diffusion model (CLD). We observe more than 20 times acceleration in BDM. In the CLD, a diffusion model by augmenting the diffusion process with velocity, our algorithm achieves an FID score of 2.26, on CIFAR10, with only 50 number of score function evaluations~(NFEs) and an FID score of 2.86 with only 27 NFEs.
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https://openreview.net/pdf/101656f96f6c22373cb9bf570b89250c966aedd5.pdf
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FedExP: Speeding Up Federated Averaging via Extrapolation
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https://openreview.net/forum?id=IPrzNbddXV
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https://openreview.net/forum?id=IPrzNbddXV
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Divyansh Jhunjhunwala,Shiqiang Wang,Gauri Joshi
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ICLR 2023,Top 25%
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Federated Averaging (FedAvg) remains the most popular algorithm for Federated Learning (FL) optimization due to its simple implementation, stateless nature, and privacy guarantees combined with secure aggregation. Recent work has sought to generalize the vanilla averaging in FedAvg to a generalized gradient descent step by treating client updates as pseudo-gradients and using a server step size. While the use of a server step size has been shown to provide performance improvement theoretically, the practical benefit of the server step size has not been seen in most existing works. In this work, we present FedExP, a method to adaptively determine the server step size in FL based on dynamically varying pseudo-gradients throughout the FL process. We begin by considering the overparameterized convex regime, where we reveal an interesting similarity between FedAvg and the Projection Onto Convex Sets (POCS) algorithm. We then show how FedExP can be motivated as a novel extension to the extrapolation mechanism that is used to speed up POCS. Our theoretical analysis later also discusses the implications of FedExP in underparameterized and non-convex settings. Experimental results show that FedExP consistently converges faster than FedAvg and competing baselines on a range of realistic FL datasets.
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https://openreview.net/pdf/8f9800051e3387ff23fc9a42a792d5ace5e665aa.pdf
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Serving Graph Compression for Graph Neural Networks
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https://openreview.net/forum?id=T-qVtA3pAxG
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https://openreview.net/forum?id=T-qVtA3pAxG
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Si Si,Felix Yu,Ankit Singh Rawat,Cho-Jui Hsieh,Sanjiv Kumar
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ICLR 2023,Top 25%
|
Serving a GNN model online is challenging --- in many applications when testing nodes are connected to training nodes, one has to propagate information from training nodes to testing nodes to achieve the best performance, and storing the whole training set (including training graph and node features) during inference stage is prohibitive for large-scale problems. In this paper, we study graph compression to reduce the storage requirement for GNN in serving. Given a GNN model to be served, we propose to construct a compressed graph with a smaller number of nodes. In serving time, one just needs to replace the original training set graph by this compressed graph, without the need of changing the actual GNN model and the forward pass. We carefully analyze the error in the forward pass and derive simple ways to construct the compressed graph to minimize the approximation error. Experimental results on semi-supervised node classification demonstrate that the proposed method can significantly reduce the serving space requirement for GNN inference.
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https://openreview.net/pdf/8063bd2baf3253b8b89e0478ff49c81f80ed3305.pdf
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Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency
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https://openreview.net/forum?id=Cs3r5KLdoj
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https://openreview.net/forum?id=Cs3r5KLdoj
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Yijun Tian,Chuxu Zhang,Zhichun Guo,Xiangliang Zhang,Nitesh Chawla
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ICLR 2023,Top 25%
|
While Graph Neural Networks (GNNs) have demonstrated their efficacy in dealing with non-Euclidean structural data, they are difficult to be deployed in real applications due to the scalability constraint imposed by the multi-hop data dependency. Existing methods attempt to address this scalability issue by training student multi-layer perceptrons (MLPs) exclusively on node content features using labels derived from the teacher GNNs. However, the trained MLPs are neither effective nor robust. In this paper, we ascribe the lack of effectiveness and robustness to three significant challenges: 1) the misalignment between content feature and label spaces, 2) the strict hard matching to teacher's output, and 3) the sensitivity to node feature noises. To address the challenges, we propose NOSMOG, a novel method to learn NOise-robust Structure-aware MLPs On Graphs, with remarkable effectiveness, robustness, and efficiency. Specifically, we first address the misalignment by complementing node content with position features to capture the graph structural information. We then design an innovative representational similarity distillation strategy to inject soft node similarities into MLPs. Finally, we introduce adversarial feature augmentation to ensure stable learning against feature noises. Extensive experiments and theoretical analyses demonstrate the superiority of NOSMOG by comparing it to GNNs and the state-of-the-art method in both transductive and inductive settings across seven datasets. Codes are available at https://github.com/meettyj/NOSMOG.
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https://openreview.net/pdf/38ae9a6d49f8c8de73c63c07dd1bb214b1e1d1e0.pdf
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Contrastive Audio-Visual Masked Autoencoder
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https://openreview.net/forum?id=QPtMRyk5rb
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https://openreview.net/forum?id=QPtMRyk5rb
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Yuan Gong,Andrew Rouditchenko,Alexander H. Liu,David Harwath,Leonid Karlinsky,Hilde Kuehne,James R. Glass
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ICLR 2023,Top 25%
|
In this paper, we first extend the recent Masked Auto-Encoder (MAE) model from a single modality to audio-visual multi-modalities. Subsequently, we propose the Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE) by combining contrastive learning and masked data modeling, two major self-supervised learning frameworks, to learn a joint and coordinated audio-visual representation.
Our experiments show that the contrastive audio-visual correspondence learning objective not only enables the model to perform audio-visual retrieval tasks, but also helps the model learn a better joint representation. As a result, our fully self-supervised pretrained CAV-MAE achieves a new SOTA accuracy of 65.9% on VGGSound, and is comparable with the previous best supervised pretrained model on AudioSet in the audio-visual event classification task. Code and pretrained models are at https://github.com/yuangongnd/cav-mae.
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https://openreview.net/pdf/6de0262994e10ffd87b06b9ad0e8b4f86c84f044.pdf
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The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks
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https://openreview.net/forum?id=IM4xp7kGI5V
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https://openreview.net/forum?id=IM4xp7kGI5V
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Daniel Kunin,Atsushi Yamamura,Chao Ma,Surya Ganguli
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ICLR 2023,Top 25%
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In this work, we explore the maximum-margin bias of quasi-homogeneous neural networks trained with gradient flow on an exponential loss and past a point of separability. We introduce the class of quasi-homogeneous models, which is expressive enough to describe nearly all neural networks with homogeneous activations, even those with biases, residual connections, and normalization layers, while structured enough to enable geometric analysis of its gradient dynamics. Using this analysis, we generalize the existing results of maximum-margin bias for homogeneous networks to this richer class of models. We find that gradient flow implicitly favors a subset of the parameters, unlike in the case of a homogeneous model where all parameters are treated equally. We demonstrate through simple examples how this strong favoritism toward minimizing an asymmetric norm can degrade the robustness of quasi-homogeneous models. On the other hand, we conjecture that this norm-minimization discards, when possible, unnecessary higher-order parameters, reducing the model to a sparser parameterization. Lastly, by applying our theorem to sufficiently expressive neural networks with normalization layers, we reveal a universal mechanism behind the empirical phenomenon of Neural Collapse.
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https://openreview.net/pdf/35e901e92e53dbfee861403dfbe3d0044bfb91a7.pdf
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Optimal Transport for Offline Imitation Learning
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https://openreview.net/forum?id=MhuFzFsrfvH
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https://openreview.net/forum?id=MhuFzFsrfvH
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Yicheng Luo,zhengyao jiang,Samuel Cohen,Edward Grefenstette,Marc Peter Deisenroth
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ICLR 2023,Top 25%
|
With the advent of large datasets, offline reinforcement learning is a promising framework for learning good decision-making policies without the need to interact with the real environment.
However, offline RL requires the dataset to be reward-annotated, which presents practical challenges when reward engineering is difficult or when obtaining reward annotations is labor-intensive.
In this paper, we introduce Optimal Transport Relabeling (OTR), an imitation learning algorithm that can automatically relabel offline data of mixed and unknown quality with rewards from a few good demonstrations. OTR's key idea is to use optimal transport to compute an optimal alignment between an unlabeled trajectory in the dataset and an expert demonstration to obtain a similarity measure that can be interpreted as a reward, which can then be used by an offline RL algorithm to learn the policy. OTR is easy to implement and computationally efficient. On D4RL benchmarks, we demonstrate that OTR with a single demonstration can consistently match the performance of offline RL with ground-truth rewards.
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https://openreview.net/pdf/3c2503af4f49d5f2f79a720075d8cfc042c50960.pdf
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Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization
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https://openreview.net/forum?id=8aHzds2uUyB
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https://openreview.net/forum?id=8aHzds2uUyB
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Rajkumar Ramamurthy,Prithviraj Ammanabrolu,Kianté Brantley,Jack Hessel,Rafet Sifa,Christian Bauckhage,Hannaneh Hajishirzi,Yejin Choi
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ICLR 2023,Top 25%
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We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If we view text generation as a sequential decision-making problem, reinforcement learning (RL) appears to be a natural conceptual framework. However, using RL for LM-based generation faces empirical challenges, including training instability due to the combinatorial action space, as well as a lack of open-source libraries and benchmarks customized for LM alignment. Thus, a question rises in the research community: is RL a practical paradigm for NLP?
To help answer this, we first introduce an open-source modular library, $RL4LMs$ (Reinforcement Learning for Language Models), for optimizing language generators with RL. The library consists of on-policy RL algorithms that can be used to train any encoder or encoder-decoder LM in the HuggingFace library (Wolf et al. 2020) with an arbitrary reward function. Next, we present the $GRUE$ (General Reinforced-language Understanding Evaluation) benchmark, a set of 6 language generation tasks which are supervised not by target strings, but by reward functions which capture automated measures of human preference.GRUE is the first leaderboard-style evaluation of RL algorithms for NLP tasks. Finally, we introduce an easy-to-use, performant RL algorithm, $NLPO$ (Natural Language Policy Optimization)} that learns to effectively reduce the combinatorial action space in language generation. We show 1) that RL techniques are generally better than supervised methods at aligning LMs to human preferences; and 2) that NLPO exhibits greater stability and performance than previous policy gradient methods (e.g., PPO (Schulman et al. 2017)), based on both automatic and human evaluations.
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https://openreview.net/pdf/e7b48c662a15dbddc1a3d5c9a2b338c13189506e.pdf
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Learning multi-scale local conditional probability models of images
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https://openreview.net/forum?id=VZX2I_VVJKH
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https://openreview.net/forum?id=VZX2I_VVJKH
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Zahra Kadkhodaie,Florentin Guth,Stéphane Mallat,Eero P Simoncelli
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ICLR 2023,Top 25%
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Deep neural networks can learn powerful prior probability models for images, as evidenced by the high-quality generations obtained with recent score-based diffusion methods. But the means by which these networks capture complex global statistical structure, apparently without suffering from the curse of dimensionality, remain a mystery. To study this, we incorporate diffusion methods into a multi-scale decomposition, reducing dimensionality by assuming a stationary local Markov model for wavelet coefficients conditioned on coarser-scale coefficients. We instantiate this model using convolutional neural networks (CNNs) with local receptive fields, which enforce both the stationarity and Markov properties. Global structures are captured using a CNN with receptive fields covering the entire (but small) low-pass image. We test this model on a dataset of face images, which are highly non-stationary and contain large-scale geometric structures.
Remarkably, denoising, super-resolution, and image synthesis results all demonstrate that these structures can be captured with significantly smaller conditioning neighborhoods than required by a Markov model implemented in the pixel domain. Our results show that score estimation for large complex images can be reduced to low-dimensional Markov conditional models across scales, alleviating the curse of dimensionality.
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https://openreview.net/pdf/df0e89dd7728d64bf15f64a0771ede4c857aad7c.pdf
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Disentanglement with Biological Constraints: A Theory of Functional Cell Types
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https://openreview.net/forum?id=9Z_GfhZnGH
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https://openreview.net/forum?id=9Z_GfhZnGH
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James C. R. Whittington,Will Dorrell,Surya Ganguli,Timothy Behrens
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ICLR 2023,Top 25%
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Neurons in the brain are often finely tuned for specific task variables. Moreover, such disentangled representations are highly sought after in machine learning. Here we mathematically prove that simple biological constraints on neurons, namely nonnegativity and energy efficiency in both activity and weights, promote such sought after disentangled representations by enforcing neurons to become selective for single factors of task variation. We demonstrate these constraints lead to disentanglement in a variety of tasks and architectures, including variational autoencoders. We also use this theory to explain why the brain partitions its cells into distinct cell types such as grid and object-vector cells, and also explain when the brain instead entangles representations in response to entangled task factors. Overall, this work provides a mathematical understanding of why single neurons in the brain often represent single human-interpretable factors, and steps towards an understanding task structure shapes the structure of brain representation.
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https://openreview.net/pdf/263cc7c08a3dc723c277184b709aa922ef1fc5d5.pdf
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Learning rigid dynamics with face interaction graph networks
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https://openreview.net/forum?id=J7Uh781A05p
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https://openreview.net/forum?id=J7Uh781A05p
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Kelsey R Allen,Yulia Rubanova,Tatiana Lopez-Guevara,William F Whitney,Alvaro Sanchez-Gonzalez,Peter Battaglia,Tobias Pfaff
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ICLR 2023,Top 25%
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Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions. While graph neural network (GNN)-based models are effective at learning to simulate complex physical dynamics, such as fluids, cloth and articulated bodies, they have been less effective and efficient on rigid-body physics, except with very simple shapes. Existing methods that model collisions through the meshes' nodes are often inaccurate because they struggle when collisions occur on faces far from nodes. Alternative approaches that represent the geometry densely with many particles are prohibitively expensive for complex shapes. Here we introduce the ``Face Interaction Graph Network'' (FIGNet) which extends beyond GNN-based methods, and computes interactions between mesh faces, rather than nodes. Compared to learned node- and particle-based methods, FIGNet is around 4x more accurate in simulating complex shape interactions, while also 8x more computationally efficient on sparse, rigid meshes. Moreover, FIGNet can learn frictional dynamics directly from real-world data, and can be more accurate than analytical solvers given modest amounts of training data. FIGNet represents a key step forward in one of the few remaining physical domains which have seen little competition from learned simulators, and offers allied fields such as robotics, graphics and mechanical design a new tool for simulation and model-based planning.
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https://openreview.net/pdf/ce59b39a6e91536851b8532e573580c0b40de1fc.pdf
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Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions
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https://openreview.net/forum?id=6iDHce-0B-a
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https://openreview.net/forum?id=6iDHce-0B-a
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Arthur Jacot
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ICLR 2023,Top 25%
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We show that the representation cost of fully connected neural networks with homogeneous nonlinearities - which describes the implicit bias in function space of networks with $L_2$-regularization or with losses such as the cross-entropy - converges as the depth of the network goes to infinity to a notion of rank over nonlinear functions. We then inquire under which conditions the global minima of the loss recover the `true' rank of the data: we show that for too large depths the global minimum will be approximately rank 1 (underestimating the rank); we then argue that there is a range of depths which grows with the number of datapoints where the true rank is recovered. Finally, we discuss the effect of the rank of a classifier on the topology of the resulting class boundaries and show that autoencoders with optimal nonlinear rank are naturally denoising.
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https://openreview.net/pdf/856dc287ae646e9b77bfe6f682ad16ce86f0e148.pdf
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Depth Separation with Multilayer Mean-Field Networks
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https://openreview.net/forum?id=uzFQpkEzOo
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https://openreview.net/forum?id=uzFQpkEzOo
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Yunwei Ren,Mo Zhou,Rong Ge
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ICLR 2023,Top 25%
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Depth separation—why a deeper network is more powerful than a shallow one—has been a major problem in deep learning theory. Previous results often focus on representation power, for example, Safran et al. (2019) constructed a function that is easy to approximate using a 3-layer network but not approximable by any 2-layer network. In this paper, we show that this separation is in fact algorithmic: one can learn the function constructed by Safran et al. (2019) using an overparametrized network with polynomially many neurons efficiently. Our result relies on a new way of extending the mean-field limit to multilayer networks, and a decomposition of loss that factors out the error introduced by the discretization of infinite-width mean-field networks.
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https://openreview.net/pdf/ceeae99121e997ccdd6046c0ebad91895125624b.pdf
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Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise
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https://openreview.net/forum?id=i_1rbq8yFWC
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https://openreview.net/forum?id=i_1rbq8yFWC
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Wenbo Gong,Joel Jennings,Cheng Zhang,Nick Pawlowski
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ICLR 2023,Top 25%
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Discovering causal relationships between different variables from time series data has been a long-standing challenge for many domains. For example, in stock markets, the announcement of acquisitions from leading companies may have immediate effects on stock prices and increase the uncertainty of the future market due to this past action. To discover causal relations in such case, the model needs to consider non-linear relations between variables, instantaneous effect and the change of noise distribution due to past actions. We name the latter as history-dependent noise. However, previous works do not offer a solution addressing all these problems together. In this paper, we propose a structural equation model, called Rhino, which combines vector auto-regression, deep learning and variational inference to model non-linear relationships with instantaneous effects while allowing the noise distribution to be modulated by history observations. Theoretically, we prove the structural identifiability of Rhino. Our empirical results from extensive synthetic experiments and two real-world benchmarks demonstrate better discovery performance compared to relevant baselines, with ablation studies revealing its robustness under model misspecification.
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https://openreview.net/pdf/fbac1494936fa6cf98eb5c4fb5a71e68dee7d101.pdf
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Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation
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https://openreview.net/forum?id=VD-AYtP0dve
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https://openreview.net/forum?id=VD-AYtP0dve
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Lorenz Kuhn,Yarin Gal,Sebastian Farquhar
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ICLR 2023,Top 25%
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We introduce a method to measure uncertainty in large language models. For tasks like question answering, it is essential to know when we can trust the natural language outputs of foundation models. We show that measuring uncertainty in natural language is challenging because of "semantic equivalence"—different sentences can mean the same thing. To overcome these challenges we introduce semantic entropy—an entropy which incorporates linguistic invariances created by shared meanings. Our method is unsupervised, uses only a single model, and requires no modifications to off-the-shelf language models. In comprehensive ablation studies we show that the semantic entropy is more predictive of model accuracy on question answering data sets than comparable baselines.
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https://openreview.net/pdf/535617ff3bfc4d3297922f5c320dee127d1ce6cc.pdf
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DINO as a von Mises-Fisher mixture model
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https://openreview.net/forum?id=cMJo1FTwBTQ
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https://openreview.net/forum?id=cMJo1FTwBTQ
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Hariprasath Govindarajan,Per Sidén,Jacob Roll,Fredrik Lindsten
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ICLR 2023,Top 25%
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Self-distillation methods using Siamese networks are popular for self-supervised pre-training. DINO is one such method based on a cross-entropy loss between $K$-dimensional probability vectors, obtained by applying a softmax function to the dot product between representations and learnt prototypes. Given the fact that the learned representations are $L^2$-normalized, we show that DINO and its derivatives, such as iBOT, can be interpreted as a mixture model of von Mises-Fisher components. With this interpretation, DINO assumes equal precision for all components when the prototypes are also $L^2$-normalized. Using this insight we propose DINO-vMF, that adds appropriate normalization constants when computing the cluster assignment probabilities. Unlike DINO, DINO-vMF is stable also for the larger ViT-Base model with unnormalized prototypes. We show that the added flexibility of the mixture model is beneficial in terms of better image representations. The DINO-vMF pre-trained model consistently performs better than DINO on a range of downstream tasks. We obtain similar improvements for iBOT-vMF vs iBOT and thereby show the relevance of our proposed modification also for other methods derived from DINO.
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https://openreview.net/pdf/7c0fa9125fa53c842a7c216fd9f1b16ee517710f.pdf
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Associative Memory Augmented Asynchronous Spatiotemporal Representation Learning for Event-based Perception
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https://openreview.net/forum?id=ZCStthyW-TD
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https://openreview.net/forum?id=ZCStthyW-TD
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Uday Kamal,Saurabh Dash,Saibal Mukhopadhyay
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ICLR 2023,Top 25%
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We propose $\textit{EventFormer}$, a computationally efficient event-based representation learning framework for asynchronously processing event camera data. EventFormer treats sparse input events as a spatially unordered set and models their spatial interactions using self-attention mechanism. An associative memory-augmented recurrent module is used to correlate with the stored representation computed from past events. A memory addressing mechanism is proposed to store and retrieve the latent states only $\textit{where}$ these events occur and update them only $\textit{when}$ they occur. The representation learning shift from input space to the latent memory space resulting in reduced computation cost for processing each event. We show that EventFormer achieves 0.5$\%$ and 9$\%$ better accuracy with 30000$\times$ and 200$\times$ less computation compared to the state-of-the-art dense and event-based method, respectively, on event-based object recognition datasets.
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https://openreview.net/pdf/5b3db5aabca57a5bd0c832c0a96cd9c75e9462bb.pdf
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SMART: Self-supervised Multi-task pretrAining with contRol Transformers
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https://openreview.net/forum?id=9piH3Hg8QEf
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https://openreview.net/forum?id=9piH3Hg8QEf
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Yanchao Sun,Shuang Ma,Ratnesh Madaan,Rogerio Bonatti,Furong Huang,Ashish Kapoor
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ICLR 2023,Top 25%
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Self-supervised pretraining has been extensively studied in language and vision domains, where a unified model can be easily adapted to various downstream tasks by pretraining representations without explicit labels. When it comes to sequential decision-making tasks, however, it is difficult to properly design such a pretraining approach that can cope with both high-dimensional perceptual information and the complexity of sequential control over long interaction horizons. The challenge becomes combinatorially more complex if we want to pretrain representations amenable to a large variety of tasks. To tackle this problem, in this work, we formulate a general pretraining-finetuning pipeline for sequential decision making, under which we propose a generic pretraining framework \textit{Self-supervised Multi-task pretrAining with contRol Transformer (SMART)}. By systematically investigating pretraining regimes, we carefully design a Control Transformer (CT) coupled with a novel control-centric pretraining objective in a self-supervised manner. SMART encourages the representation to capture the common essential information relevant to short-term control and long-term control, which is transferrable across tasks. We show by extensive experiments in DeepMind Control Suite that SMART significantly improves the learning efficiency among seen and unseen downstream tasks and domains under different learning scenarios including Imitation Learning (IL) and Reinforcement Learning (RL). Benefiting from the proposed control-centric objective, SMART is resilient to distribution shift between pretraining and finetuning, and even works well with low-quality pretraining datasets that are randomly collected. The codebase, pretrained models and datasets are provided at https://github.com/microsoft/smart.
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https://openreview.net/pdf/0bed689d4b0c72cb2f2561862218853290e48ce5.pdf
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TEMPERA: Test-Time Prompt Editing via Reinforcement Learning
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https://openreview.net/forum?id=gSHyqBijPFO
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https://openreview.net/forum?id=gSHyqBijPFO
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Tianjun Zhang,Xuezhi Wang,Denny Zhou,Dale Schuurmans,Joseph E. Gonzalez
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ICLR 2023,Top 25%
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Careful prompt design is critical to the use of large language models in zero-shot or few-shot learning. As a consequence, there is a growing interest in automated methods to design optimal prompts. In this work, we propose Test-time Prompt Editing using Reinforcement learning (TEMPERA). In contrast to prior prompt generation methods, TEMPERA can efficiently leverage prior knowledge, is adaptive to different queries and provides an interpretable prompt for every query. To achieve this, we design a novel action space that allows flexible editing of the initial prompts covering a wide set of commonly-used components like instructions, few-shot exemplars, and verbalizers. The proposed method achieves significant gains compared with recent SoTA approaches like prompt tuning, AutoPrompt, and RLPrompt, across a variety of tasks including sentiment analysis, topic classification, natural language inference, and reading comprehension. Our method achieves 5.33x on average improvement in sample efficiency when compared to the traditional fine-tuning methods.
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https://openreview.net/pdf/1cf8438e4df114f4ea13408da5952d5636eabb99.pdf
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Provable Defense Against Geometric Transformations
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https://openreview.net/forum?id=ThXqBsRI-cY
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https://openreview.net/forum?id=ThXqBsRI-cY
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Rem Yang,Jacob Laurel,Sasa Misailovic,Gagandeep Singh
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ICLR 2023,Top 25%
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Geometric image transformations that arise in the real world, such as scaling and rotation, have been shown to easily deceive deep neural networks (DNNs). Hence, training DNNs to be certifiably robust to these perturbations is critical. However, no prior work has been able to incorporate the objective of deterministic certified robustness against geometric transformations into the training procedure, as existing verifiers are exceedingly slow. To address these challenges, we propose the first provable defense for deterministic certified geometric robustness. Our framework leverages a novel GPU-optimized verifier that can certify images between 60$\times$ to 42,600$\times$ faster than existing geometric robustness verifiers, and thus unlike existing works, is fast enough for use in training. Across multiple datasets, our results show that networks trained via our framework consistently achieve state-of-the-art deterministic certified geometric robustness and clean accuracy. Furthermore, for the first time, we verify the geometric robustness of a neural network for the challenging, real-world setting of autonomous driving.
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https://openreview.net/pdf/5f45f6724426cb1196f8d2e3bd7a227feec2b203.pdf
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Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations
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https://openreview.net/forum?id=Zb6c8A-Fghk
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https://openreview.net/forum?id=Zb6c8A-Fghk
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Polina Kirichenko,Pavel Izmailov,Andrew Gordon Wilson
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ICLR 2023,Top 25%
|
Neural network classifiers can largely rely on simple spurious features, such as image backgrounds, to make predictions. However, even in these cases, we show that they still often learn core features associated with the desired attributes of the data, contrary to recent findings. Inspired by this insight, we demonstrate that simple last layer retraining can match or outperform state-of-the-art approaches on spurious correlation benchmarks, but with profoundly lower complexity and computational expenses. Moreover, we show that last layer retraining on large ImageNet-trained models can also significantly reduce reliance on background and texture information, improving robustness to covariate shift, after only minutes of training on a single GPU.
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https://openreview.net/pdf/f0a7c2996eab5d56791cb5d7feebe23aa9ca27c4.pdf
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Towards Interpretable Deep Reinforcement Learning with Human-Friendly Prototypes
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https://openreview.net/forum?id=hWwY_Jq0xsN
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https://openreview.net/forum?id=hWwY_Jq0xsN
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Eoin M. Kenny,Mycal Tucker,Julie Shah
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ICLR 2023,Top 25%
|
Despite recent success of deep learning models in research settings, their application in sensitive domains remains limited because of their opaque decision-making processes. Taking to this challenge, people have proposed various eXplainable AI (XAI) techniques designed to calibrate trust and understandability of black-box models, with the vast majority of work focused on supervised learning. Here, we focus on making an "interpretable-by-design" deep reinforcement learning agent which is forced to use human-friendly prototypes in its decisions, thus making its reasoning process clear. Our proposed method, dubbed Prototype-Wrapper Network (PW-Net), wraps around any neural agent backbone, and results indicate that it does not worsen performance relative to black-box models. Most importantly, we found in a user study that PW-Nets supported better trust calibration and task performance relative to standard interpretability approaches and black-boxes.
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https://openreview.net/pdf/89dc907add1447b8730b63f4562410d7d9346676.pdf
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The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry
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https://openreview.net/forum?id=P4MUGRM4Acu
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https://openreview.net/forum?id=P4MUGRM4Acu
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Dian Wang,Jung Yeon Park,Neel Sortur,Lawson L.S. Wong,Robin Walters,Robert Platt
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ICLR 2023,Top 25%
|
Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by enforcing an inductive bias in the network architecture. These applications typically assume that the domain symmetry is fully described by explicit transformations of the model inputs and outputs. However, many real-life applications contain only latent or partial symmetries which cannot be easily described by simple transformations of the input. In these cases, it is necessary to learn symmetry in the environment instead of imposing it mathematically on the network architecture. We discover, surprisingly, that imposing equivariance constraints that do not exactly match the domain symmetry is very helpful in learning the true symmetry in the environment. We differentiate between extrinsic and incorrect symmetry constraints and show that while imposing incorrect symmetry can impede the model's performance, imposing extrinsic symmetry can actually improve performance. We demonstrate that an equivariant model can significantly outperform non-equivariant methods on domains with latent symmetries both in supervised learning and in reinforcement learning for robotic manipulation and control problems.
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https://openreview.net/pdf/e89d07093752cddf94cde0b8379f518fd4d8e233.pdf
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Task-customized Masked Autoencoder via Mixture of Cluster-conditional Experts
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https://openreview.net/forum?id=j8IiQUM33s
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https://openreview.net/forum?id=j8IiQUM33s
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Zhili LIU,Kai Chen,Jianhua Han,Lanqing HONG,Hang Xu,Zhenguo Li,James Kwok
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ICLR 2023,Top 25%
|
Masked Autoencoder (MAE) is a prevailing self-supervised learning method that achieves promising results in model pre-training. However, when the various downstream tasks have data distributions different from the pre-training data, the semantically irrelevant pre-training information might result in negative transfer, impeding MAE’s scalability. To address this issue, we propose a novel MAE-based pre-training paradigm, Mixture of Cluster-conditional Experts (MoCE), which can be trained once but provides customized pre-training models for diverse downstream tasks. Different from the mixture of experts (MoE), our MoCE trains each expert only with semantically relevant images by using cluster-conditional gates. Thus, each downstream task can be allocated to its customized model pre-trained with data most similar to the downstream data. Experiments on a collection of 11 downstream tasks show that MoCE outperforms the vanilla MAE by 2.45\% on average. It also obtains new state-of-the-art self-supervised learning results on detection and segmentation.
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https://openreview.net/pdf/d19031c830199ada819326ce9c24b0d2f7cb6cf4.pdf
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Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization
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https://openreview.net/forum?id=uyqks-LILZX
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https://openreview.net/forum?id=uyqks-LILZX
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Jivat Neet Kaur,Emre Kiciman,Amit Sharma
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ICLR 2023,Top 25%
|
Recent empirical studies on domain generalization (DG) have shown that DG algorithms that perform well on some distribution shifts fail on others, and no state-of-the-art DG algorithm performs consistently well on all shifts. Moreover, real-world data often has multiple distribution shifts over different attributes; hence we introduce multi-attribute distribution shift datasets and find that the accuracy of existing DG algorithms falls even further. To explain these results, we provide a formal characterization of generalization under multi-attribute shifts using a canonical causal graph. Based on the relationship between spurious attributes and the classification label, we obtain realizations of the canonical causal graph that characterize common distribution shifts and show that each shift entails different independence constraints over observed variables. As a result, we prove that any algorithm based on a single, fixed constraint cannot work well across all shifts, providing theoretical evidence for mixed empirical results on DG algorithms. Based on this insight, we develop Causally Adaptive Constraint Minimization (CACM), an algorithm that uses knowledge about the data-generating process to adaptively identify and apply the correct independence constraints for regularization. Results on fully synthetic, MNIST, small NORB, and Waterbirds datasets, covering binary and multi-valued attributes and labels, show that adaptive dataset-dependent constraints lead to the highest accuracy on unseen domains whereas incorrect constraints fail to do so. Our results demonstrate the importance of modeling the causal relationships inherent in the data-generating process.
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https://openreview.net/pdf/4048ed797cd1ecee3eb0807128459f763f1cd777.pdf
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Using Language to Extend to Unseen Domains
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https://openreview.net/forum?id=eR2dG8yjnQ
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https://openreview.net/forum?id=eR2dG8yjnQ
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Lisa Dunlap,Clara Mohri,Devin Guillory,Han Zhang,Trevor Darrell,Joseph E. Gonzalez,Aditi Raghunathan,Anna Rohrbach
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ICLR 2023,Top 25%
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It is expensive to collect training data for every possible domain that a vision model may encounter when deployed. We instead consider how simply $\textit{verbalizing}$ the training domain (e.g.``photos of birds'') as well as domains we want to extend to but do not have data for (e.g.``paintings of birds'') can improve robustness. Using a multimodal model with a joint image and language embedding space, our method $\textit{LADS}$ learns a transformation of the image embeddings from the source domain to each target domain, while preserving task relevant information. Without using any images from the target domain, we show that over the $\textit{extended}$ domain containing both source and target, $\textit{LADS}$ outperforms standard fine-tuning and ensemble approaches over a suite of 4 benchmarks targeting domain adaptation and dataset bias.
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https://openreview.net/pdf/bb5314efba6d37a2ea4f8cdbdeccd9351dde3016.pdf
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Can We Find Nash Equilibria at a Linear Rate in Markov Games?
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https://openreview.net/forum?id=eQzLwwGyQrb
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https://openreview.net/forum?id=eQzLwwGyQrb
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Zhuoqing Song,Jason D. Lee,Zhuoran Yang
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ICLR 2023,Top 25%
|
We study decentralized learning in two-player zero-sum discounted Markov games where the goal is to design a policy optimization algorithm for either agent satisfying two properties. First, the player does not need to know the policy of the opponent to update its policy. Second, when both players adopt the algorithm, their joint policy converges to a Nash equilibrium of the game. To this end, we construct a meta-algorithm, dubbed as $\texttt{Homotopy-PO}$, which provably finds a Nash equilibrium at a global linear rate. In particular, $\texttt{Homotopy-PO}$ interweaves two base algorithms $\texttt{Local-Fast}$ and $\texttt{Global-Slow}$ via homotopy continuation. $\texttt{Local-Fast}$ is an algorithm that enjoys local linear convergence while $\texttt{Global-Slow}$ is an algorithm that converges globally but at a slower sublinear rate. By switching between these two base algorithms, $\texttt{Global-Slow}$ essentially serves as a ``guide'' which identifies a benign neighborhood where $\texttt{Local-Fast}$ enjoys fast convergence. However, since the exact size of such a neighborhood is unknown, we apply a doubling trick to switch between these two base algorithms. The switching scheme is delicately designed so that the aggregated performance of the algorithm is driven by $\texttt{Local-Fast}$. Furthermore, we prove that $\texttt{Local-Fast}$ and $\texttt{Global-Slow}$ can both be instantiated by variants of optimistic gradient descent/ascent (OGDA) method, which is of independent interest.
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https://openreview.net/pdf/f6285c79bac699974cbfa7334d3e42800b338c64.pdf
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Hebbian Deep Learning Without Feedback
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https://openreview.net/forum?id=8gd4M-_Rj1
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https://openreview.net/forum?id=8gd4M-_Rj1
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Adrien Journé,Hector Garcia Rodriguez,Qinghai Guo,Timoleon Moraitis
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ICLR 2023,Top 25%
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Recent approximations to backpropagation (BP) have mitigated many of BP's computational inefficiencies and incompatibilities with biology, but important limitations still remain. Moreover, the approximations significantly decrease accuracy in benchmarks, suggesting that an entirely different approach may be more fruitful. Here, grounded on recent theory for Hebbian learning in soft winner-take-all networks, we present multilayer SoftHebb, i.e. an algorithm that trains deep neural networks, without any feedback, target, or error signals. As a result, it achieves efficiency by avoiding weight transport, non-local plasticity, time-locking of layer updates, iterative equilibria, and (self-) supervisory or other feedback signals – which were necessary in other approaches. Its increased efficiency and biological compatibility do not trade off accuracy compared to state-of-the-art bio-plausible learning, but rather improve it. With up to five hidden layers and an added linear classifier, accuracies on MNIST, CIFAR-10, STL-10, and ImageNet, respectively reach 99.4%, 80.3%, 76.2%, and 27.3%. In conclusion, SoftHebb shows with a radically different approach from BP that Deep Learning over few layers may be plausible in the brain and increases the accuracy of bio-plausible machine learning. Code is available at https://github.com/NeuromorphicComputing/SoftHebb.
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https://openreview.net/pdf/8069b75c93174254f8042cc114dad9bbd5b73989.pdf
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A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation
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https://openreview.net/forum?id=kt-dcBQcSA
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https://openreview.net/forum?id=kt-dcBQcSA
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Edoardo Balzani,Jean-Paul G Noel,Pedro Herrero-Vidal,Dora E Angelaki,Cristina Savin
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ICLR 2023,Top 25%
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Latent manifolds provide a compact characterization of neural population activity and of shared co-variability across brain areas. Nonetheless, existing statistical tools for extracting neural manifolds face limitations in terms of interpretability of latents with respect to task variables, and can be hard to apply to datasets with no trial repeats. Here we propose a novel probabilistic framework that allows for interpretable partitioning of population variability within and across areas in the context of naturalistic behavior. Our approach for task aligned manifold estimation (TAME-GP) explicitly partitions variability into private and shared sources which can themselves be subdivided in task-relevant and task irrelevant components, uses a realistic Poisson noise model, and introduces temporal smoothing of latent trajectories in the form of a Gaussian Process prior. This TAME-GP graphical model allows for robust estimation of task-relevant variability in local population responses, and of shared co-variability between brain areas. We demonstrate the efficiency of our estimator on within model and biologically motivated simulated data. We also apply it to several datasets of neural population recordings during behavior. Overall, our results demonstrate the capacity of TAME-GP to capture meaningful intra- and inter-area neural variability with single trial resolution.
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https://openreview.net/pdf/9e46e84807837851d4357f969ea06aa885cf4f5a.pdf
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Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics
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https://openreview.net/forum?id=PvLnIaJbt9
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https://openreview.net/forum?id=PvLnIaJbt9
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Shoaib Ahmed Siddiqui,Nitarshan Rajkumar,Tegan Maharaj,David Krueger,Sara Hooker
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ICLR 2023,Top 25%
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Modern machine learning research relies on relatively few carefully curated datasets. Even in these datasets, and typically in `untidy' or raw data, practitioners are faced with significant issues of data quality and diversity which can be prohibitively labor intensive to address. Existing methods for dealing with these challenges tend to make strong assumptions about the particular issues at play, and often require a priori knowledge or metadata such as domain labels. Our work is orthogonal to these methods: we instead focus on providing a unified and efficient framework for Metadata Archaeology -- uncovering and inferring metadata of examples in a dataset. We curate different subsets of data that might exist in a dataset (e.g. mislabeled, atypical, or out-of-distribution examples) using simple transformations, and leverage differences in learning dynamics between these probe suites to infer metadata of interest. Our method is on par with far more sophisticated mitigation methods across different tasks: identifying and correcting mislabeled examples, classifying minority-group samples, prioritizing points relevant for training and enabling scalable human auditing of relevant examples.
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https://openreview.net/pdf/a2556369723de05dfed6645f353856e0f08459a8.pdf
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Proposal-Contrastive Pretraining for Object Detection from Fewer Data
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https://openreview.net/forum?id=gm0VZ-h-hPy
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https://openreview.net/forum?id=gm0VZ-h-hPy
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Quentin Bouniot,Romaric Audigier,Angelique Loesch,Amaury Habrard
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ICLR 2023,Top 25%
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The use of pretrained deep neural networks represents an attractive way to achieve strong results with few data available. When specialized in dense problems such as object detection, learning local rather than global information in images has proven to be more efficient. However, for unsupervised pretraining, the popular contrastive learning requires a large batch size and, therefore, a lot of resources. To address this problem, we are interested in transformer-based object detectors that have recently gained traction in the community with good performance and with the particularity of generating many diverse object proposals.
In this work, we present Proposal Selection Contrast (ProSeCo), a novel unsupervised overall pretraining approach that leverages this property. ProSeCo uses the large number of object proposals generated by the detector for contrastive learning, which allows the use of a smaller batch size, combined with object-level features to learn local information in the images. To improve the effectiveness of the contrastive loss, we introduce the object location information in the selection of positive examples to take into account multiple overlapping object proposals. When reusing pretrained backbone, we advocate for consistency in learning local information between the backbone and the detection head.
We show that our method outperforms state of the art in unsupervised pretraining for object detection on standard and novel benchmarks in learning with fewer data.
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https://openreview.net/pdf/409d076ce5528382d9695b832fdb2bebf4977300.pdf
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ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations
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https://openreview.net/forum?id=HXz7Vcm3VgM
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https://openreview.net/forum?id=HXz7Vcm3VgM
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Badr Youbi Idrissi,Diane Bouchacourt,Randall Balestriero,Ivan Evtimov,Caner Hazirbas,Nicolas Ballas,Pascal Vincent,Michal Drozdzal,David Lopez-Paz,Mark Ibrahim
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ICLR 2023,Top 25%
|
Deep learning vision systems are widely deployed across applications where reliability is critical. However, even today's best models can fail to recognize an object when its pose, lighting, or background varies. While existing benchmarks surface examples challenging for models, they do not explain why such mistakes arise. To address this need, we introduce ImageNet-X—a set of sixteen human annotations of factors such as pose, background, or lighting the entire ImageNet-1k validation set as well as a random subset of 12k training images. Equipped with ImageNet-X, we investigate 2,200 current recognition models and study the types of mistakes as a function of model’s (1) architecture, e.g. transformer vs. convolutional, (2) learning paradigm, e.g. supervised vs. self-supervised, and (3) training procedures, e.g., data augmentation. Regardless of these choices, we find models have consistent failure modes across ImageNet-X categories. We also find that while data augmentation can improve robustness to certain factors, they induce spill-over effects to other factors. For example, color-jitter augmentation improves robustness to color and brightness, but surprisingly hurts robustness to pose. Together, these insights suggest to advance the robustness of modern vision models, future research should focus on collecting additional data and understanding data augmentation schemes. Along with these insights, we release a toolkit based on ImageNet-X to spur further study into the mistakes image recognition systems make.
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https://openreview.net/pdf/33fbd8d8db5f01fb273b222f69e3719afd7a9778.pdf
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Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries
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https://openreview.net/forum?id=b7SBTEBFnC
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https://openreview.net/forum?id=b7SBTEBFnC
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Yuxin Wen,Arpit Bansal,Hamid Kazemi,Eitan Borgnia,Micah Goldblum,Jonas Geiping,Tom Goldstein
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ICLR 2023,Top 25%
|
As industrial applications are increasingly automated by machine learning models, enforcing personal data ownership and intellectual property rights requires tracing training data back to their rightful owners. Membership inference algorithms approach this problem by using statistical techniques to discern whether a target sample was included in a model's training set. However, existing methods only utilize the unaltered target sample or simple augmentations of the target to compute statistics. Such a sparse sampling of the model's behavior carries little information, leading to poor inference capabilities. In this work, we use adversarial tools to directly optimize for queries that are discriminative and diverse. Our improvements achieve significantly more accurate membership inference than existing methods, especially in offline scenarios and in the low false-positive regime which is critical in legal settings.
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https://openreview.net/pdf/77e3d4474830ef1ec482bf7015ca9b61fa9f8264.pdf
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Choreographer: Learning and Adapting Skills in Imagination
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https://openreview.net/forum?id=PhkWyijGi5b
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https://openreview.net/forum?id=PhkWyijGi5b
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Pietro Mazzaglia,Tim Verbelen,Bart Dhoedt,Alexandre Lacoste,Sai Rajeswar
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ICLR 2023,Top 25%
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Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with the ability to control and influence the environment. However, without appropriate knowledge and exploration, skills may provide control only over a restricted area of the environment, limiting their applicability. Furthermore, it is unclear how to leverage the learned skill behaviors for adapting to downstream tasks in a data-efficient manner. We present Choreographer, a model-based agent that exploits its world model to learn and adapt skills in imagination. Our method decouples the exploration and skill learning processes, being able to discover skills in the latent state space of the model. During adaptation, the agent uses a meta-controller to evaluate and adapt the learned skills efficiently by deploying them in parallel in imagination. Choreographer is able to learn skills both from offline data, and by collecting data simultaneously with an exploration policy. The skills can be used to effectively adapt to downstream tasks, as we show in the URL benchmark, where we outperform previous approaches from both pixels and states inputs. The skills also explore the environment thoroughly, finding sparse rewards more frequently, as shown in goal-reaching tasks from the DMC Suite and Meta-World.
Project website: https://skillchoreographer.github.io/
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https://openreview.net/pdf/3b9c0c356a7760d7f70096b567ff2aef51b26f98.pdf
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Learning About Progress From Experts
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https://openreview.net/forum?id=sKc6fgce1zs
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https://openreview.net/forum?id=sKc6fgce1zs
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Jake Bruce,Ankit Anand,Bogdan Mazoure,Rob Fergus
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ICLR 2023,Top 25%
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Many important tasks involve some notion of long-term progress in multiple phases: e.g. to clean a shelf it must be cleared of items, cleaning products applied, and then the items placed back on the shelf. In this work, we explore the use of expert demonstrations in long-horizon tasks to learn a monotonically increasing function that summarizes progress. This function can then be used to aid agent exploration in environments with sparse rewards. As a case study we consider the NetHack environment, which requires long-term progress at a variety of scales and is far from being solved by existing approaches. In this environment, we demonstrate that by learning a model of long-term progress from expert data containing only observations, we can achieve efficient exploration in challenging sparse tasks, well beyond what is possible with current state-of-the-art approaches. We have made the curated gameplay dataset used in this work available at https://github.com/deepmind/nao_top10.
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https://openreview.net/pdf/6c536f4956e9ba6881ac817930a21ec1d8526220.pdf
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Learning Fair Graph Representations via Automated Data Augmentations
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https://openreview.net/forum?id=1_OGWcP1s9w
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https://openreview.net/forum?id=1_OGWcP1s9w
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Hongyi Ling,Zhimeng Jiang,Youzhi Luo,Shuiwang Ji,Na Zou
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ICLR 2023,Top 25%
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We consider fair graph representation learning via data augmentations. While this direction has been explored previously, existing methods invariably rely on certain assumptions on the properties of fair graph data in order to design fixed strategies on data augmentations. Nevertheless, the exact properties of fair graph data may vary significantly in different scenarios. Hence, heuristically designed augmentations may not always generate fair graph data in different application scenarios. In this work, we propose a method, known as Graphair, to learn fair representations based on automated graph data augmentations. Such fairness-aware augmentations are themselves learned from data. Our Graphair is designed to automatically discover fairness-aware augmentations from input graphs in order to circumvent sensitive information while preserving other useful information. Experimental results demonstrate that our Graphair consistently outperforms many baselines on multiple node classification datasets in terms of fairness-accuracy trade-off performance. In addition, results indicate that Graphair can automatically learn to generate fair graph data without prior knowledge on fairness-relevant graph properties.
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https://openreview.net/pdf/6bbe51476329fdae6f372b5d0c4f4e4051745eeb.pdf
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Emergence of Maps in the Memories of Blind Navigation Agents
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https://openreview.net/forum?id=lTt4KjHSsyl
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https://openreview.net/forum?id=lTt4KjHSsyl
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Erik Wijmans,Manolis Savva,Irfan Essa,Stefan Lee,Ari S. Morcos,Dhruv Batra
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ICLR 2023,Top 25%
|
Animal navigation research posits that organisms build and maintain internal spa- tial representations, or maps, of their environment. We ask if machines – specifically, artificial intelligence (AI) navigation agents – also build implicit (or ‘mental’) maps. A positive answer to this question would (a) explain the surprising phenomenon in recent literature of ostensibly map-free neural-networks achieving strong performance, and (b) strengthen the evidence of mapping as a fundamental mechanism for navigation by intelligent embodied agents, whether they be biological or artificial. Unlike animal navigation, we can judiciously design the agent’s perceptual system and control the learning paradigm to nullify alternative navigation mechanisms. Specifically, we train ‘blind’ agents – with sensing limited to only egomotion and no other sensing of any kind – to perform PointGoal navigation (‘go to $\Delta$x, $\Delta$y’) via reinforcement learning. Our agents are composed of navigation-agnostic components (fully-connected and recurrent neural networks), and our experimental setup provides no inductive bias towards mapping. Despite these harsh conditions, we find that blind agents are (1) surprisingly effective navigators in new environments (∼95% success); (2) they utilize memory over long horizons (remembering ∼1,000 steps of past experience in an episode); (3) this memory enables them to exhibit intelligent behavior (following walls, detecting collisions, taking shortcuts); (4) there is emergence of maps and collision detection neurons in the representations of the environment built by a blind agent as it navigates; and (5) the emergent maps are selective and task dependent (e.g. the agent ‘forgets’ exploratory detours). Overall, this paper presents no new techniques for the AI audience, but a surprising finding, an insight, and an explanation.
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https://openreview.net/pdf/6aff51942ab3664378283e5da2b36db1cd04db62.pdf
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Spectral Augmentation for Self-Supervised Learning on Graphs
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https://openreview.net/forum?id=DjzBCrMBJ_p
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https://openreview.net/forum?id=DjzBCrMBJ_p
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Lu Lin,Jinghui Chen,Hongning Wang
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ICLR 2023,Top 25%
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Graph contrastive learning (GCL), as an emerging self-supervised learning technique on graphs, aims to learn representations via instance discrimination. Its performance heavily relies on graph augmentation to reflect invariant patterns that are robust to small perturbations; yet it still remains unclear about what graph invariance GCL should capture. Recent studies mainly perform topology augmentations in a uniformly random manner in the spatial domain, ignoring its influence on the intrinsic structural properties embedded in the spectral domain. In this work, we aim to find a principled way for topology augmentations by exploring the invariance of graphs from the spectral perspective. We develop spectral augmentation which guides topology augmentations by maximizing the spectral change. Extensive experiments on both graph and node classification tasks demonstrate the effectiveness of our method in self-supervised representation learning. The proposed method also brings promising generalization capability in transfer learning, and is equipped with intriguing robustness property under adversarial attacks. Our study sheds light on a general principle for graph topology augmentation.
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https://openreview.net/pdf/f3bc720be318c5e2d1b97759ef657ead63c87974.pdf
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VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation
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https://openreview.net/forum?id=WOquZTLCBO1
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https://openreview.net/forum?id=WOquZTLCBO1
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Thanh Nguyen-Tang,Raman Arora
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ICLR 2023,Top 25%
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We propose a novel algorithm for offline reinforcement learning called Value Iteration with Perturbed Rewards (VIPeR), which amalgamates the pessimism principle with random perturbations of the value function. Most current offline RL algorithms explicitly construct statistical confidence regions to obtain pessimism via lower confidence bounds (LCB), which cannot easily scale to complex problems where a neural network is used to estimate the value functions. Instead, VIPeR implicitly obtains pessimism by simply perturbing the offline data multiple times with carefully-designed i.i.d. Gaussian noises to learn an ensemble of estimated state-action {value functions} and acting greedily with respect to the minimum of the ensemble. The estimated state-action values are obtained by fitting a parametric model (e.g., neural networks) to the perturbed datasets using gradient descent. As a result, VIPeR only needs $\mathcal{O}(1)$ time complexity for action selection, while LCB-based algorithms require at least $\Omega(K^2)$, where $K$ is the total number of trajectories in the offline data. We also propose a novel data-splitting technique that helps remove a factor involving the log of the covering number in our bound. We prove that VIPeR yields a provable uncertainty quantifier with overparameterized neural networks and enjoys a bound on sub-optimality of $\tilde{\mathcal{O}}( { \kappa H^{5/2} \tilde{d} }/{\sqrt{K}})$, where $\tilde{d}$ is the effective dimension, $H$ is the horizon length and $\kappa$ measures the distributional shift. We corroborate the statistical and computational efficiency of VIPeR with an empirical evaluation on a wide set of synthetic and real-world datasets. To the best of our knowledge, VIPeR is the first algorithm for offline RL that is provably efficient for general Markov decision processes (MDPs) with neural network function approximation.
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https://openreview.net/pdf/9a8eb48070ffcd0332fa35d7aaa9229cea1438c4.pdf
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Self-supervised learning with rotation-invariant kernels
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https://openreview.net/forum?id=8uu6JStuYm
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https://openreview.net/forum?id=8uu6JStuYm
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Léon Zheng,Gilles Puy,Elisa Riccietti,Patrick Perez,Rémi Gribonval
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ICLR 2023,Top 25%
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We introduce a regularization loss based on kernel mean embeddings with rotation-invariant kernels on the hypersphere (also known as dot-product kernels) for self-supervised learning of image representations. Besides being fully competitive with the state of the art, our method significantly reduces time and memory complexity for self-supervised training, making it implementable for very large embedding dimensions on existing devices and more easily adjustable than previous methods to settings with limited resources. Our work follows the major paradigm where the model learns to be invariant to some predefined image transformations (cropping, blurring, color jittering, etc.), while avoiding a degenerate solution by regularizing the embedding distribution. Our particular contribution is to propose a loss family promoting the embedding distribution to be close to the uniform distribution on the hypersphere, with respect to the maximum mean discrepancy pseudometric. We demonstrate that this family encompasses several regularizers of former methods, including uniformity-based and information-maximization methods, which are variants of our flexible regularization loss with different kernels. Beyond its practical consequences for state of the art self-supervised learning with limited resources, the proposed generic regularization approach opens perspectives to leverage more widely the literature on kernel methods in order to improve self-supervised learning methods.
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https://openreview.net/pdf/ef737455df9ff0ef5ebc958acb84991ccf4647e6.pdf
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Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity
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https://openreview.net/forum?id=QubsmJT_A0
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https://openreview.net/forum?id=QubsmJT_A0
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Deniz Oktay,Mehran Mirramezani,Eder Medina,Ryan P Adams
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ICLR 2023,Top 25%
|
Intelligent biological systems are characterized by their embodiment in a complex environment and the intimate interplay between their nervous systems and the nonlinear mechanical properties of their bodies. This coordination, in which the dynamics of the motor system co-evolved to reduce the computational burden on the brain, is referred to as "mechanical intelligence" or "morphological computation". In this work, we seek to develop machine learning analogs of this process, in which we jointly learn the morphology of complex nonlinear elastic solids along with a deep neural network to control it. By using a specialized differentiable simulator of elastic mechanics coupled to conventional deep learning architectures---which we refer to as neuromechanical autoencoders---we are able to learn to perform morphological computation via gradient descent. Key to our approach is the use of mechanical metamaterials---cellular solids, in particular---as the morphological substrate. Just as deep neural networks provide flexible and massively-parametric function approximators for perceptual and control tasks, cellular solid metamaterials are promising as a rich and learnable space for approximating a variety of actuation tasks. In this work we take advantage of these complementary computational concepts to co-design materials and neural network controls to achieve nonintuitive mechanical behavior. We demonstrate in simulation how it is possible to achieve translation, rotation, and shape matching, as well as a "digital MNIST" task. We additionally manufacture and evaluate one of the designs to verify its real-world behavior.
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https://openreview.net/pdf/04faa9edbfcb1a55394ac2bf8a342b32638ba5a2.pdf
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VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
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https://openreview.net/forum?id=YJ7o2wetJ2
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https://openreview.net/forum?id=YJ7o2wetJ2
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Yecheng Jason Ma,Shagun Sodhani,Dinesh Jayaraman,Osbert Bastani,Vikash Kumar,Amy Zhang
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ICLR 2023,Top 25%
|
Reward and representation learning are two long-standing challenges for learning an expanding set of robot manipulation skills from sensory observations. Given the inherent cost and scarcity of in-domain, task-specific robot data, learning from large, diverse, offline human videos has emerged as a promising path towards acquiring a generally useful visual representation for control; however, how these human videos can be used for general-purpose reward learning remains an open question. We introduce $\textbf{V}$alue-$\textbf{I}$mplicit $\textbf{P}$re-training (VIP), a self-supervised pre-trained visual representation capable of generating dense and smooth reward functions for unseen robotic tasks. VIP casts representation learning from human videos as an offline goal-conditioned reinforcement learning problem and derives a self-supervised dual goal-conditioned value-function objective that does not depend on actions, enabling pre-training on unlabeled human videos. Theoretically, VIP can be understood as a novel implicit time contrastive objective that generates a temporally smooth embedding, enabling the value function to be implicitly defined via the embedding distance, which can then be used to construct the reward for any goal-image specified downstream task. Trained on large-scale Ego4D human videos and without any fine-tuning on in-domain, task-specific data, VIP can provide dense visual reward for an extensive set of simulated and $\textbf{real-robot}$ tasks, enabling diverse reward-based visual control methods and significantly outperforming all prior pre-trained representations. Notably, VIP can enable simple, few-shot offline RL on a suite of real-world robot tasks with as few as 20 trajectories.
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https://openreview.net/pdf/56f5b528ba9ae4f7c40ca328636fffe7c8c0c7da.pdf
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Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation
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https://openreview.net/forum?id=74A-FDAyiL
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https://openreview.net/forum?id=74A-FDAyiL
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Ainesh Bakshi,Piotr Indyk,Praneeth Kacham,Sandeep Silwal,Samson Zhou
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ICLR 2023,Top 25%
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Kernel matrices, as well as weighted graphs represented by them, are ubiquitous objects in machine learning, statistics and other related fields. The main drawback of using kernel methods (learning and inference using kernel matrices) is efficiency -- given $n$ input points, most kernel-based algorithms need to materialize the full $n \times n$ kernel matrix before performing any subsequent computation, thus incurring $\Omega(n^2)$ runtime. Breaking this quadratic barrier for various problems has therefore, been a subject of extensive research efforts.
We break the quadratic barrier and obtain \emph{subquadratic} time algorithms for several fundamental linear-algebraic and graph processing primitives, including approximating the top eigenvalue and eigenvector, spectral sparsification, solving linear systems, local clustering, low-rank approximation, arboricity estimation and counting weighted triangles. We build on the recently developed Kernel Density Estimation framework, which (after preprocessing in time subquadratic in $n$) can return estimates of row/column sums of the kernel matrix. In particular, we develop efficient reductions from \emph{weighted vertex} and \emph{weighted edge sampling} on kernel graphs, \emph{simulating random walks} on kernel graphs, and \emph{importance sampling} on matrices to Kernel Density Estimation and show that we can generate samples from these distributions in \emph{sublinear} (in the support of the distribution) time. Our reductions are the central ingredient in each of our applications and we believe they may be of independent interest. We empirically demonstrate the efficacy of our algorithms on low-rank approximation (LRA) and spectral sparsification, where we observe a $\textbf{9x}$ decrease in the number of kernel evaluations over baselines for LRA and a $\textbf{41x}$ reduction in the graph size for spectral sparsification.
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https://openreview.net/pdf/93ea27d82a23d0825a29366c4dc3af3944c6d41a.pdf
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A Higher Precision Algorithm for Computing the $1$-Wasserstein Distance
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https://openreview.net/forum?id=aMXD8gqsIiC
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https://openreview.net/forum?id=aMXD8gqsIiC
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Pankaj K Agarwal,Sharath Raghvendra,Pouyan Shirzadian,Rachita Sowle
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ICLR 2023,Top 25%
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We consider the problem of computing the $1$-Wasserstein distance $\mathcal{W}(\mu,\nu)$ between two $d$-dimensional discrete distributions $\mu$ and $\nu$ whose support lie within the unit hypercube. There are several algorithms that estimate $\mathcal{W}(\mu,\nu)$ within an additive error of $\varepsilon$. However, when $\mathcal{W}(\mu,\nu)$ is small, the additive error $\varepsilon$ dominates, leading to noisy results. Consider any additive approximation algorithm with execution time $T(n,\varepsilon)$. We propose an algorithm that runs in $O(T(n,\varepsilon/d) \log n)$ time and boosts the accuracy of estimating $\mathcal{W}(\mu,\nu)$ from $\varepsilon$ to an expected additive error of $\min\{\varepsilon, (d\log_{\sqrt{d}/\varepsilon} n)\mathcal{W}(\mu,\nu)\}$. For the special case where every point in the support of $\mu$ and $\nu$ has a mass of $1/n$ (also called the Euclidean Bipartite Matching problem), we describe an algorithm to boost the accuracy of any additive approximation algorithm from $\varepsilon$ to an expected additive error of $\min\{\varepsilon, (d\log\log n)\mathcal{W}(\mu,\nu)\}$ in $O(T(n, \varepsilon/d)\log\log n)$ time.
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https://openreview.net/pdf/469fe4170141940b63599e6d0d1e5b3a205619b5.pdf
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Revisiting adapters with adversarial training
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https://openreview.net/forum?id=HPdxC1THU8T
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https://openreview.net/forum?id=HPdxC1THU8T
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Sylvestre-Alvise Rebuffi,Francesco Croce,Sven Gowal
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ICLR 2023,Top 25%
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While adversarial training is generally used as a defense mechanism, recent works show that it can also act as a regularizer. By co-training a neural network on clean and adversarial inputs, it is possible to improve classification accuracy on the clean, non-adversarial inputs. We demonstrate that, contrary to previous findings, it is not necessary to separate batch statistics when co-training on clean and adversarial inputs, and that it is sufficient to use adapters with few domain-specific parameters for each type of input. We establish that using the classification token of a Vision Transformer (ViT) as an adapter is enough to match the classification performance of dual normalization layers, while using significantly less additional parameters. First, we improve upon the top-1 accuracy of a non-adversarially trained ViT-B16 model by +1.12% on ImageNet (reaching 83.76% top-1 accuracy). Second, and more importantly, we show that training with adapters enables model soups through linear combinations of the clean and adversarial tokens. These model soups, which we call adversarial model soups, allow us to trade-off between clean and robust accuracy without sacrificing efficiency. Finally, we show that we can easily adapt the resulting models in the face of distribution shifts. Our ViT-B16 obtains top-1 accuracies on ImageNet variants that are on average +4.00% better than those obtained with Masked Autoencoders.
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https://openreview.net/pdf/c986093cab366dcc82865df98b5906e39dc7c493.pdf
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UNICORN: A Unified Backdoor Trigger Inversion Framework
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https://openreview.net/forum?id=Mj7K4lglGyj
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https://openreview.net/forum?id=Mj7K4lglGyj
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Zhenting Wang,Kai Mei,Juan Zhai,Shiqing Ma
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ICLR 2023,Top 25%
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The backdoor attack, where the adversary uses inputs stamped with triggers (e.g., a patch) to activate pre-planted malicious behaviors, is a severe threat to Deep Neural Network (DNN) models. Trigger inversion is an effective way of identifying backdoor models and understanding embedded adversarial behaviors. A challenge of trigger inversion is that there are many ways of constructing the trigger. Existing methods cannot generalize to various types of triggers by making certain assumptions or attack-specific constraints. The fundamental reason is that existing work does not formally define the trigger and the inversion problem. This work formally defines and analyzes the trigger and the inversion problem. Then, it proposes a unified framework to invert backdoor triggers based on the formalization of triggers and the identified inner behaviors of backdoor models from our analysis. Our prototype UNICORN is general and effective in inverting backdoor triggers in DNNs. The code can be found at https://github.com/RU-System-Software-and-Security/UNICORN.
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https://openreview.net/pdf/edd35173abda536a0bd486d49c34c8ce04e56652.pdf
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ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion
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https://openreview.net/forum?id=xkev3_np08z
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https://openreview.net/forum?id=xkev3_np08z
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Aleksandar Pavlović,Emanuel Sallinger
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ICLR 2023,Top 25%
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Knowledge graphs are inherently incomplete. Therefore substantial research has been directed toward knowledge graph completion (KGC), i.e., predicting missing triples from the information represented in the knowledge graph (KG). KG embedding models (KGEs) have yielded promising results for KGC, yet any current KGE is incapable of: (1) fully capturing vital inference patterns (e.g., composition), (2) capturing prominent patterns jointly (e.g., hierarchy and composition), and (3) providing an intuitive interpretation of captured patterns. In this work, we propose ExpressivE, a fully expressive spatio-functional KGE that solves all these challenges simultaneously. ExpressivE embeds pairs of entities as points and relations as hyper-parallelograms in the virtual triple space $\mathbb{R}^{2d}$. This model design allows ExpressivE not only to capture a rich set of inference patterns jointly but additionally to display any supported inference pattern through the spatial relation of hyper-parallelograms, offering an intuitive and consistent geometric interpretation of ExpressivE embeddings and their captured patterns. Experimental results on standard KGC benchmarks reveal that ExpressivE is competitive with state-of-the-art KGEs and even significantly outperforms them on WN18RR.
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https://openreview.net/pdf/071ed2e450ebd00e88fdcae80a0773cfe4c7aec8.pdf
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Localized Randomized Smoothing for Collective Robustness Certification
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https://openreview.net/forum?id=-k7Lvk0GpBl
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https://openreview.net/forum?id=-k7Lvk0GpBl
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Jan Schuchardt,Tom Wollschläger,Aleksandar Bojchevski,Stephan Günnemann
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ICLR 2023,Top 25%
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Models for image segmentation, node classification and many other tasks map a single input to multiple labels. By perturbing this single shared input (e.g. the image) an adversary can manipulate several predictions (e.g. misclassify several pixels). Collective robustness certification is the task of provably bounding the number of robust predictions under this threat model. The only dedicated method that goes beyond certifying each output independently is limited to strictly local models, where each prediction is associated with a small receptive field. We propose a more general collective robustness certificate for all types of models. We further show that this approach is beneficial for the larger class of softly local models, where each output is dependent on the entire input but assigns different levels of importance to different input regions (e.g. based on their proximity in the image). The certificate is based on our novel localized randomized smoothing approach, where the random perturbation strength for different input regions is proportional to their importance for the outputs. Localized smoothing Pareto-dominates existing certificates on both image segmentation and node classification tasks, simultaneously offering higher accuracy and stronger certificates.
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https://openreview.net/pdf/2c33160f207d6fbfbc89af90d5f1b6d98446dab7.pdf
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Learning Probabilistic Topological Representations Using Discrete Morse Theory
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https://openreview.net/forum?id=cXMHQD-xQas
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https://openreview.net/forum?id=cXMHQD-xQas
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Xiaoling Hu,Dimitris Samaras,Chao Chen
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ICLR 2023,Top 25%
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Accurate delineation of fine-scale structures is a very important yet challenging problem. Existing methods use topological information as an additional training loss, but are ultimately making pixel-wise predictions. In this paper, we propose a novel deep learning based method to learn topological/structural. We use discrete Morse theory and persistent homology to construct a one-parameter family of structures as the topological/structural representation space. Furthermore, we learn a probabilistic model that can perform inference tasks in such a topological/structural representation space. Our method generates true structures rather than pixel-maps, leading to better topological integrity in automatic segmentation tasks. It also facilitates semi-automatic interactive annotation/proofreading via the sampling of structures and structure-aware uncertainty.
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https://openreview.net/pdf/b5fd2b7efdc313e009b70b9dfc6af81a7350ff8e.pdf
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Model-based Causal Bayesian Optimization
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https://openreview.net/forum?id=Vk-34OQ7rFo
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https://openreview.net/forum?id=Vk-34OQ7rFo
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Scott Sussex,Anastasia Makarova,Andreas Krause
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ICLR 2023,Top 25%
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How should we intervene on an unknown structural equation model to maximize a downstream variable of interest? This setting, also known as causal Bayesian optimization (CBO), has important applications in medicine, ecology, and manufacturing. Standard Bayesian optimization algorithms fail to effectively leverage the underlying causal structure. Existing CBO approaches assume noiseless measurements and do not come with guarantees. We propose the {\em model-based causal Bayesian optimization algorithm (MCBO)} that learns a full system model instead of only modeling intervention-reward pairs. MCBO propagates epistemic uncertainty about the causal mechanisms through the graph and trades off exploration and exploitation via the optimism principle. We bound its cumulative regret, and obtain the first non-asymptotic bounds for CBO. Unlike in standard Bayesian optimization, our acquisition function cannot be evaluated in closed form, so we show how the reparameterization trick can be used to apply gradient-based optimizers. The resulting practical implementation of MCBO compares favorably with state-of-the-art approaches empirically.
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https://openreview.net/pdf/4d05ca91171a6278984e77236a0ead44b9d44e48.pdf
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Training language models to summarize narratives improves brain alignment
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https://openreview.net/forum?id=KzkLAE49H9b
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https://openreview.net/forum?id=KzkLAE49H9b
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Khai Loong Aw,Mariya Toneva
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ICLR 2023,Top 25%
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Building systems that achieve a deeper understanding of language is one of the central goals of natural language processing (NLP). Towards this goal, recent works have begun to train language models on narrative datasets which require extracting the most critical information by integrating across long contexts. However, it is still an open question whether these models are learning a deeper understanding of the text, or if the models are simply learning a heuristic to complete the task. This work investigates this further by turning to the one language processing system that truly understands complex language: the human brain. We show that training language models for deeper narrative understanding results in richer representations that have improved alignment to human brain activity. We further find that the improvements in brain alignment are larger for character names than for other discourse features, which indicates that these models are learning important narrative elements. Taken together, these results suggest that this type of training can indeed lead to deeper language understanding. These findings have consequences both for cognitive neuroscience by revealing some of the significant factors behind brain-NLP alignment, and for NLP by highlighting that understanding of long-range context can be improved beyond language modeling.
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https://openreview.net/pdf/c67334d169d975ca4c1f56fc722f9eb680ebf5b9.pdf
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Dual Algorithmic Reasoning
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https://openreview.net/forum?id=hhvkdRdWt1F
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https://openreview.net/forum?id=hhvkdRdWt1F
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Danilo Numeroso,Davide Bacciu,Petar Veličković
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ICLR 2023,Top 25%
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Neural Algorithmic Reasoning is an emerging area of machine learning which seeks to infuse algorithmic computation in neural networks, typically by training neural models to approximate steps of classical algorithms. In this context, much of the current work has focused on learning reachability and shortest path graph algorithms, showing that joint learning on similar algorithms is beneficial for generalisation. However, when targeting more complex problems, such "similar" algorithms become more difficult to find. Here, we propose to learn algorithms by exploiting duality of the underlying algorithmic problem. Many algorithms solve optimisation problems. We demonstrate that simultaneously learning the dual definition of these optimisation problems in algorithmic learning allows for better learning and qualitatively better solutions. Specifically, we exploit the max-flow min-cut theorem to simultaneously learn these two algorithms over synthetically generated graphs, demonstrating the effectiveness of the proposed approach. We then validate the real-world utility of our dual algorithmic reasoner by deploying it on a challenging brain vessel classification task, which likely depends on the vessels’ flow properties. We demonstrate a clear performance gain when using our model within such a context, and empirically show that learning the max-flow and min-cut algorithms together is critical for achieving such a result.
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https://openreview.net/pdf/68736260b81982cea120df8994f055abbfe1ec5c.pdf
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A Primal-Dual Framework for Transformers and Neural Networks
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https://openreview.net/forum?id=U_T8-5hClV
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https://openreview.net/forum?id=U_T8-5hClV
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Tan Minh Nguyen,Tam Minh Nguyen,Nhat Ho,Andrea L. Bertozzi,Richard Baraniuk,Stanley Osher
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ICLR 2023,Top 25%
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Self-attention is key to the remarkable success of transformers in sequence modeling tasks including many applications in natural language processing and computer vision. Like neural network layers, these attention mechanisms are often developed by heuristics and experience. To provide a principled framework for constructing attention layers in transformers, we show that the self-attention corresponds to the support vector expansion derived from a support vector regression problem, whose primal formulation has the form of a neural network layer. Using our framework, we derive popular attention layers used in practice and propose two new attentions: 1) the Batch Normalized Attention (Attention-BN) derived from the batch normalization layer and 2) the Attention with Scaled Head (Attention-SH) derived from using less training data to fit the SVR model. We empirically demonstrate the advantages of the Attention-BN and Attention-SH in reducing head redundancy, increasing the model's accuracy, and improving the model's efficiency in a variety of practical applications including image and time-series classification.
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https://openreview.net/pdf/ea60565f7f50777889e3d7d4e95d5feb7f8df5cb.pdf
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Fisher-Legendre (FishLeg) optimization of deep neural networks
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https://openreview.net/forum?id=c9lAOPvQHS
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https://openreview.net/forum?id=c9lAOPvQHS
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Jezabel R Garcia,Federica Freddi,Stathi Fotiadis,Maolin Li,Sattar Vakili,Alberto Bernacchia,Guillaume Hennequin
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ICLR 2023,Top 25%
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Incorporating second-order gradient information (curvature) into optimization can dramatically reduce the number of iterations required to train machine learning models. In natural gradient descent, such information comes from the Fisher information matrix which yields a number of desirable properties. As exact natural gradient updates are intractable for large models, successful methods such as KFAC and sequels approximate the Fisher in a structured form that can easily be inverted. However, this requires model/layer-specific tensor algebra and certain approximations that are often difficult to justify. Here, we use ideas from Legendre-Fenchel duality to learn a direct and efficiently evaluated model for the product of the inverse Fisher with any vector, in an online manner, leading to natural gradient steps that get progressively more accurate over time despite noisy gradients. We prove that the resulting “Fisher-Legendre” (FishLeg) optimizer converges to a (global) minimum of non-convex functions satisfying the PL condition, which applies in particular to deep linear networks. On standard auto-encoder benchmarks, we show empirically that FishLeg outperforms standard first-order optimization methods, and performs on par with or better than other second-order methods, especially when using small batches. Thanks to its generality, we expect our approach to facilitate the handling of a variety neural network layers in future work.
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https://openreview.net/pdf/7d525e39743734fab17afec726da606418b81613.pdf
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Capturing the Motion of Every Joint: 3D Human Pose and Shape Estimation with Independent Tokens
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https://openreview.net/forum?id=0Vv4H4Ch0la
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https://openreview.net/forum?id=0Vv4H4Ch0la
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Sen Yang,Wen Heng,Gang Liu,GUOZHONG LUO,Wankou Yang,Gang YU
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ICLR 2023,Top 25%
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In this paper we present a novel method to estimate 3D human pose and shape from monocular videos. This task requires directly recovering pixel-alignment 3D human pose and body shape from monocular images or videos, which is challenging due to its inherent ambiguity. To improve precision, existing methods highly rely on the initialized mean pose and shape as prior estimates and parameter regression with an iterative error feedback manner. In addition, video-based approaches model the overall change over the image-level features to temporally enhance the single-frame feature, but fail to capture the rotational motion at the joint level, and cannot guarantee local temporal consistency. To address these issues, we propose a novel Transformer-based model with a design of independent tokens. First, we introduce three types of tokens independent of the image feature: \textit{joint rotation tokens, shape token, and camera token}.
By progressively interacting with image features through Transformer layers, these tokens learn to encode the prior knowledge of human 3D joint rotations, body shape, and position information from large-scale data, and are updated to estimate SMPL parameters conditioned on a given image. Second, benefiting from the proposed token-based representation, we further use a temporal model to focus on capturing the rotational temporal information of each joint, which is empirically conducive to preventing large jitters in local parts. Despite being conceptually simple, the proposed method attains superior performances on the 3DPW and Human3.6M datasets. Using ResNet-50 and Transformer architectures, it obtains 42.0 mm error on the PA-MPJPE metric of the challenging 3DPW, outperforming state-of-the-art counterparts by a large margin. Code will be publicly available\footnote{\url{https://github.com/yangsenius/INT_HMR_Model}}.
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https://openreview.net/pdf/ac15036f14fc083ad641661d544085d5eaefef81.pdf
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Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
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https://openreview.net/forum?id=lJdOlWg8td
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https://openreview.net/forum?id=lJdOlWg8td
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Anand Subramoney,Khaleelulla Khan Nazeer,Mark Schöne,Christian Mayr,David Kappel
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ICLR 2023,Top 25%
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Recurrent neural networks (RNNs) are well suited for solving sequence tasks in resource-constrained systems due to their expressivity and low computational requirements. However, there is still a need to bridge the gap between what RNNs are capable of in terms of efficiency and performance and real-world application requirements. The memory and computational requirements arising from propagating the activations of all the neurons at every time step to every connected neuron, together with the sequential dependence of activations, contribute to the inefficiency of training and using RNNs. We propose a solution inspired by biological neuron dynamics that makes the communication between RNN units sparse and discrete. This makes the backward pass with backpropagation through time (BPTT) computationally sparse and efficient as well. We base our model on the gated recurrent unit (GRU), extending it with units that emit discrete events for communication triggered by a threshold so that no information is communicated to other units in the absence of events. We show theoretically that the communication between units, and hence the computation required for both the forward and backward passes, scales with the number of events in the network. Our model achieves efficiency without compromising task performance, demonstrating competitive performance compared to state-of-the-art recurrent network models in real-world tasks, including language modeling. The dynamic activity sparsity mechanism also makes our model well suited for novel energy-efficient neuromorphic hardware. Code is available at https://github.com/KhaleelKhan/EvNN/.
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https://openreview.net/pdf/388663b8b91354ae6d68fc7eb3580a2b5f3431fa.pdf
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Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
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https://openreview.net/forum?id=XVjTT1nw5z
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https://openreview.net/forum?id=XVjTT1nw5z
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Xingchao Liu,Chengyue Gong,qiang liu
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ICLR 2023,Top 25%
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We present rectified flow, a simple approach to learning (neural) ordinary differential equation (ODE) models to transport between two empirically observed distributions $\pi_0$ and $\pi_1$, hence providing a unified solution to generative modeling and domain transfer, among various other tasks involving distribution transport. The idea of rectified flow is to learn the ODE to follow the straight paths connecting the points drawn from $\pi_0$ and $\pi_1$ as much as possible. This is achieved by solving a straightforward nonlinear least squares optimization problem, which can be easily scaled to large models without introducing extra parameters beyond standard supervised learning. The straight paths are the shortest paths between two points, and can be simulated exactly without time discretization and hence yield computationally efficient models. We show that, by learning a rectified flow from data, we effectively turn an arbitrary coupling of $\pi_0$ and $\pi_1$ to a new deterministic coupling with provably non-increasing convex transport costs. In addition, with a ``reflow" procedure that iteratively learns a new rectified flow from the data bootstrapped from the previous one, we obtain a sequence of flows with increasingly straight paths, which can be simulated accurately with coarse time discretization in the inference phase. In empirical studies, we show that rectified flow performs superbly on image generation, image-to-image translation, and domain adaptation. In particular, on image generation and translation, our method yields nearly straight flows that give high quality results even with \emph{a single Euler discretization step}. Code is available at \url{https://github.com/gnobitab/RectifiedFlow}.
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https://openreview.net/pdf/910c5efa5739a5d2bef83d432da87d3096712ebe.pdf
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Inequality phenomenon in $l_{\infty}$-adversarial training, and its unrealized threats
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https://openreview.net/forum?id=4t9q35BxGr
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https://openreview.net/forum?id=4t9q35BxGr
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Ranjie Duan,YueFeng Chen,Yao Zhu,Xiaojun Jia,Rong Zhang,Hui Xue'
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ICLR 2023,Top 25%
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The appearance of adversarial examples raises attention from both academia and industry. Along with the attack-defense arms race, adversarial training is the most effective against adversarial examples.
However, we find inequality phenomena occur during the $l_{\infty}$-adversarial training, that few features dominate the prediction made by the adversarially trained model. We systematically evaluate such inequality phenomena by extensive experiments and find such phenomena become more obvious when performing adversarial training with increasing adversarial strength (evaluated by $\epsilon$). We hypothesize such inequality phenomena make $l_{\infty}$-adversarially trained model less reliable than the standard trained model when few ``important features" are influenced. To validate our hypothesis, we proposed two simple attacks that either perturb or replace important features with noise or occlusion. Experiments show that $l_{\infty}$-adversarially trained model can be easily attacked when the few important features are influenced.
Our work shed light on the limitation of the practicality of $l_{\infty}$-adversarial training.
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https://openreview.net/pdf/7ab5da22ccb9ac23b9d16fdd5e20f7d2d5da1b17.pdf
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Learning Diffusion Bridges on Constrained Domains
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https://openreview.net/forum?id=WH1yCa0TbB
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https://openreview.net/forum?id=WH1yCa0TbB
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Xingchao Liu,Lemeng Wu,Mao Ye,qiang liu
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ICLR 2023,Top 25%
|
Diffusion models have achieved promising results on generative learning recently. However, because diffusion processes are most naturally applied on the unconstrained Euclidean space $\mathrm{R}^d$, key challenges arise for developing diffusion based models for learning data on constrained and structured domains. We present a simple and unified framework to achieve this that can be easily adopted to various types of domains, including product spaces of any type (be it bounded/unbounded, continuous/discrete, categorical/ordinal, or their mix). In our model, the diffusion process is driven by a drift force that is a sum of two terms: one singular force designed by $Doob's~ h$-$transform$ that ensures all outcomes of the process to belong to the desirable domain, and one non-singular neural force field that is trained to make sure the outcome follows the data distribution statistically. Experiments show that our methods perform superbly on generating tabular data, images, semantic segments and 3D point clouds.
|
https://openreview.net/pdf/8cfc259b7bd52dd062abbfdf800fc63779766d37.pdf
|
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations
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https://openreview.net/forum?id=1_jFneF07YC
|
https://openreview.net/forum?id=1_jFneF07YC
|
Andrii Zadaianchuk,Matthaeus Kleindessner,Yi Zhu,Francesco Locatello,Thomas Brox
|
ICLR 2023,Top 25%
|
In this paper, we show that recent advances in self-supervised representation learning enable unsupervised object discovery and semantic segmentation with a performance that matches the state of the field on supervised semantic segmentation 10 years ago. We propose a methodology based on unsupervised saliency masks and self-supervised feature clustering to kickstart object discovery followed by training a semantic segmentation network on pseudo-labels to bootstrap the system on images with multiple objects. We show that while being conceptually simple our proposed baseline is surprisingly strong. We present results on PASCAL VOC that go far beyond the current state of the art (50.0 mIoU), and we report for the first time results on MS COCO for the whole set of 81 classes: our method discovers 34 categories with more than 20% IoU, while obtaining an average IoU of 19.6 for all 81 categories.
|
https://openreview.net/pdf/7b1fa6ddb8a0a13864f94d60cd015ca2274147b9.pdf
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Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning
|
https://openreview.net/forum?id=f0a_dWEYg-Td
|
https://openreview.net/forum?id=f0a_dWEYg-Td
|
Hao He,Kaiwen Zha,Dina Katabi
|
ICLR 2023,Top 25%
|
Indiscriminate data poisoning attacks are quite effective against supervised learning. However, not much is known about their impact on unsupervised contrastive learning (CL). This paper is the first to consider indiscriminate poisoning attacks of contrastive learning. We propose Contrastive Poisoning (CP), the first effective such attack on CL. We empirically show that Contrastive Poisoning, not only drastically reduces the performance of CL algorithms, but also attacks supervised learning models, making it the most generalizable indiscriminate poisoning attack. We also show that CL algorithms with a momentum encoder are more robust to indiscriminate poisoning, and propose a new countermeasure based on matrix completion. Code is available at: https://github.com/kaiwenzha/contrastive-poisoning.
|
https://openreview.net/pdf/06017158fae111af5eb2c5e44b5b2c0c9a8d4526.pdf
|
Decompositional Generation Process for Instance-Dependent Partial Label Learning
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https://openreview.net/forum?id=lKOfilXucGB
|
https://openreview.net/forum?id=lKOfilXucGB
|
Congyu Qiao,Ning Xu,Xin Geng
|
ICLR 2023,Top 25%
|
Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels and model the generation process of the candidate labels in a simple way. However, these approaches usually do not perform as well as expected due to the fact that the generation process of the candidate labels is always instance-dependent. Therefore, it deserves to be modeled in a refined way. In this paper, we consider instance-dependent PLL and assume that the generation process of the candidate labels could decompose into two sequential parts, where the correct label emerges first in the mind of the annotator but then the incorrect labels related to the feature are also selected with the correct label as candidate labels due to uncertainty of labeling. Motivated by this consideration, we propose a novel PLL method that performs Maximum A Posterior(MAP) based on an explicitly modeled generation process of candidate labels via decomposed probability distribution models. Extensive experiments on manually corrupted benchmark datasets and real-world datasets validate the effectiveness of the proposed method.
|
https://openreview.net/pdf/e5ce2d07910993cab2778e9cb83312b7e51599e4.pdf
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Building a Subspace of Policies for Scalable Continual Learning
|
https://openreview.net/forum?id=UKr0MwZM6fL
|
https://openreview.net/forum?id=UKr0MwZM6fL
|
Jean-Baptiste Gaya,Thang Doan,Lucas Caccia,Laure Soulier,Ludovic Denoyer,Roberta Raileanu
|
ICLR 2023,Top 25%
|
The ability to continuously acquire new knowledge and skills is crucial for autonomous agents. Existing methods are typically based on either fixed-size models that struggle to learn a large number of diverse behaviors, or growing-size models that scale poorly with the number of tasks. In this work, we aim to strike a better balance between scalability and performance by designing a method whose size grows adaptively depending on the task sequence. We introduce Continual Subspace of Policies (CSP), a new approach that incrementally builds a subspace of policies for training a reinforcement learning agent on a sequence of tasks. The subspace's high expressivity allows CSP to perform well for many different tasks while growing more slowly than the number of tasks. Our method does not suffer from forgetting and also displays positive transfer to new tasks. CSP outperforms a number of popular baselines on a wide range of scenarios from two challenging domains, Brax (locomotion) and Continual World (robotic manipulation). Interactive visualizations of the subspace can be found at https://share.streamlit.io/continual-subspace/policies/main.
|
https://openreview.net/pdf/ab8b649c5427a1281f061c035a6c4c3a82699f57.pdf
|
Not All Tasks Are Born Equal: Understanding Zero-Shot Generalization
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https://openreview.net/forum?id=KGV-GBh8fb
|
https://openreview.net/forum?id=KGV-GBh8fb
|
Jing Zhou,Zongyu Lin,Yanan Zheng,Jian Li,Zhilin Yang
|
ICLR 2023,Top 25%
|
Recent work has achieved remarkable zero-shot performance with multi-task prompted pretraining, but little has been understood. For the first time, we show that training on a small number of key tasks beats using all the training tasks, while removing these key tasks substantially hurts performance. We also find that these key tasks are mostly question answering (QA) tasks. These novel findings combined deepen our understanding about zero-shot generalization—training on certain tasks such as QA encodes general knowledge transferable to a wide range of tasks. In addition, to automate this procedure, we devise a method that (1) identifies key training tasks without observing the test tasks by examining the pairwise generalization results and (2) resamples training tasks for better data distribution. Empirically, our approach achieves improved results across various model scales and tasks.
|
https://openreview.net/pdf/1919d0fcb91288c02556462ba37d25845db2452f.pdf
|
Solving Constrained Variational Inequalities via a First-order Interior Point-based Method
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https://openreview.net/forum?id=RQY2AXFMRiu
|
https://openreview.net/forum?id=RQY2AXFMRiu
|
Tong Yang,Michael Jordan,Tatjana Chavdarova
|
ICLR 2023,Top 25%
|
We develop an interior-point approach to solve constrained variational inequality (cVI) problems. Inspired by the efficacy of the alternating direction method of multipliers (ADMM) method in the single-objective context, we generalize ADMM to derive a first-order method for cVIs, that we refer to as ADMM-based interior-point method for constrained VIs (ACVI). We provide convergence guarantees for ACVI in two general classes of problems: (i) when the operator is $\xi$-monotone, and (ii) when it is monotone, some constraints are active and the game is not purely rotational. When the operator is in addition L-Lipschitz for the latter case, we match known lower bounds on rates for the gap function of $\mathcal{O}(1/\sqrt{K})$ and $\mathcal{O}(1/K)$ for the last and average iterate, respectively. To the best of our knowledge, this is the first presentation of a first-order interior-point method for the general cVI problem that has a global convergence guarantee. Moreover, unlike previous work in this setting, ACVI provides a means to solve cVIs when the constraints are nontrivial. Empirical analyses demonstrate clear advantages of ACVI over common first-order methods. In particular, (i) cyclical behavior is notably reduced as our methods approach the solution from the analytic center, and (ii) unlike projection-based methods that zigzag when near a constraint, ACVI efficiently handles the constraints.
|
https://openreview.net/pdf/7210cea0f1893a45604d73d4a566c6e439c1046f.pdf
|
Symmetric Pruning in Quantum Neural Networks
|
https://openreview.net/forum?id=K96AogLDT2K
|
https://openreview.net/forum?id=K96AogLDT2K
|
Xinbiao Wang,Junyu Liu,Tongliang Liu,Yong Luo,Yuxuan Du,Dacheng Tao
|
ICLR 2023,Top 25%
|
Many fundamental properties of a quantum system are captured by its Hamiltonian and ground state. Despite the significance, ground states preparation (GSP) is classically intractable for large-scale Hamiltonians. Quantum neural networks (QNNs), which exert the power of modern quantum machines, have emerged as a leading protocol to conquer this issue. As such, the performance enhancement of QNNs becomes the core in GSP. Empirical evidence showed that QNNs with handcraft symmetric ans\"atze generally experience better trainability than those with asymmetric ans\"atze, while theoretical explanations remain vague. To fill this knowledge gap, here we propose the effective quantum neural tangent kernel (EQNTK) and connect this concept with over-parameterization theory to quantify the convergence of QNNs towards the global optima. We uncover that the advance of symmetric ans\"atze attributes to their large EQNTK value with low effective dimension, which requests few parameters and quantum circuit depth to reach the over-parameterization regime permitting a benign loss landscape and fast convergence. Guided by EQNTK, we further devise a symmetric pruning (SP) scheme to automatically tailor a symmetric ansatz from an over-parameterized and asymmetric one to greatly improve the performance of QNNs when the explicit symmetry information of Hamiltonian is unavailable. Extensive numerical simulations are conducted to validate the analytical results of EQNTK and the effectiveness of SP.
|
https://openreview.net/pdf/985ba693c6dc7d26909831bb66906ae4f3810a91.pdf
|
Minimum Variance Unbiased N:M Sparsity for the Neural Gradients
|
https://openreview.net/forum?id=vuD2xEtxZcj
|
https://openreview.net/forum?id=vuD2xEtxZcj
|
Brian Chmiel,Itay Hubara,Ron Banner,Daniel Soudry
|
ICLR 2023,Top 25%
|
In deep learning, fine-grained N:M sparsity reduces the data footprint and bandwidth of a General Matrix multiply (GEMM) up to x2, and doubles throughput by skipping computation of zero values. So far, it was mainly only used to prune weights to accelerate the forward and backward phases. We examine how this method can be used also for the neural gradients (i.e. loss gradients with respect to the intermediate neural layer outputs). To this end, we first establish a tensor-level optimality criteria. Previous works aimed to minimize the mean-square-error (MSE) of each pruned block. We show that while minimization of the MSE works fine for pruning the weights and activations, it catastrophically fails for the neural gradients. Instead, we show that accurate pruning of the neural gradients requires an unbiased minimum-variance pruning mask. We design such specialized masks, and find that in most cases, 1:2 sparsity is sufficient for training, and 2:4 sparsity is usually enough when this is not the case. Further, we suggest combining several such methods together in order to potentially speed up training even more. A reference implementation is supplied in the supplementary material.
|
https://openreview.net/pdf/b7b54047fdaf97f505713a0b6675abc7e120c460.pdf
|
Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models
|
https://openreview.net/forum?id=a2jNdqE2102
|
https://openreview.net/forum?id=a2jNdqE2102
|
Xiaoman Pan,Wenlin Yao,Hongming Zhang,Dian Yu,Dong Yu,Jianshu Chen
|
ICLR 2023,Top 25%
|
Fully-parametric language models generally require a huge number of model parameters to store the necessary knowledge for solving multiple natural language tasks in zero/few-shot settings. In addition, it is hard to adapt to the evolving world knowledge without the costly model re-training. In this paper, we develop a novel semi-parametric language model architecture, Knowledge-in-Context (KiC), which empowers a parametric text-to-text language model with a knowledge-rich external memory. Specifically, the external memory contains six different types of knowledge: entity, dictionary, commonsense, event, script, and causality knowledge. For each input instance, the KiC model adaptively selects a knowledge type and retrieves the most helpful pieces of knowledge. The input instance along with its knowledge augmentation is fed into a text-to-text model (e.g., T5) to generate the output answer, where both the input and the output are in natural language forms after prompting. Interestingly, we find that KiC can be identified as a special mixture-of-experts (MoE) model, where the knowledge selector plays the role of a router that is used to determine the sequence-to-expert assignment in MoE. This key observation inspires us to develop a novel algorithm for training KiC with an instance-adaptive knowledge selector. As a knowledge-rich semi-parametric language model, KiC only needs a much smaller parametric part to achieve superior zero-shot performance on unseen tasks. By evaluating on 40+ different tasks, we show that KiC-Large with 770M parameters easily outperforms large language models that are 4-39x larger. In addition, KiC also exhibits emergent abilities at a much smaller model scale compared to the fully-parametric models.
|
https://openreview.net/pdf/2f1a38a721bba2dcfa96af632678ce02c41b26bd.pdf
|
Mosaic Representation Learning for Self-supervised Visual Pre-training
|
https://openreview.net/forum?id=JAezPMehaUu
|
https://openreview.net/forum?id=JAezPMehaUu
|
Zhaoqing Wang,Ziyu Chen,Yaqian Li,Yandong Guo,Jun Yu,Mingming Gong,Tongliang Liu
|
ICLR 2023,Top 25%
|
Self-supervised learning has achieved significant success in learning visual representations without the need for manual annotation. To obtain generalizable representations, a meticulously designed data augmentation strategy is one of the most crucial parts. Recently, multi-crop strategies utilizing a set of small crops as positive samples have been shown to learn spatially structured features. However, it overlooks the diverse contextual backgrounds, which reduces the variance of the input views and degenerates the performance. To address this problem, we propose a mosaic representation learning framework (MosRep), consisting of a new data augmentation strategy that enriches the backgrounds of each small crop and improves the quality of visual representations. Specifically, we randomly sample numbers of small crops from different input images and compose them into a mosaic view, which is equivalent to introducing different background information for each small crop. Additionally, we further jitter the mosaic view to prevent memorizing the spatial locations of each crop. Along with optimization, our MosRep gradually extracts more discriminative features. Extensive experimental results demonstrate that our method improves the performance far greater than the multi-crop strategy on a series of downstream tasks, e.g., +7.4% and +4.9% than the multi-crop strategy on ImageNet-1K with 1% label and 10% label, respectively. Code is available at https://github.com/DerrickWang005/MosRep.git.
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https://openreview.net/pdf/08d8aac0aaab473c9858de17885ada84ced0e940.pdf
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FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation
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https://openreview.net/forum?id=Cp-io_BoFaE
|
https://openreview.net/forum?id=Cp-io_BoFaE
|
Zhou Xian,Bo Zhu,Zhenjia Xu,Hsiao-Yu Tung,Antonio Torralba,Katerina Fragkiadaki,Chuang Gan
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ICLR 2023,Top 25%
|
Humans manipulate various kinds of fluids in their everyday life: creating latte art, scooping floating objects from water, rolling an ice cream cone, etc. Using robots to augment or replace human labors in these daily settings remain as a challenging task due to the multifaceted complexities of fluids. Previous research in robotic fluid manipulation mostly consider fluids governed by an ideal, Newtonian model in simple task settings (e.g., pouring water into a container). However, the vast majority of real-world fluid systems manifest their complexities in terms of the fluid’s complex material behaviors (e.g., elastoplastic deformation) and multi-component interactions (e.g. coffee and frothed milk when making latte art), both of which were well beyond the scope of the current literature. To evaluate robot learning algorithms on understanding and interacting with such complex fluid systems, a comprehensive virtual platform with versatile simulation capabilities and well-established tasks is needed. In this work, we introduce FluidLab, a simulation environment with a diverse set of manipulation tasks involving complex fluid dynamics. These tasks address interactions between solid and fluid as well as among multiple fluids. At the heart of our platform is a fully differentiable physics simulator, FluidEngine, providing GPU-accelerated simulations and gradient calculations for various material types and their couplings, extending the scope of the existing differentiable simulation engines. We identify several challenges for fluid manipulation learning by evaluating a set of reinforcement learning and trajectory optimization methods on our platform. To address these challenges, we propose several domain-specific optimization schemes coupled with differentiable physics, which are empirically shown to be effective in tackling optimization problems featured by fluid system’s non-convex and non-smooth properties. Furthermore, we demonstrate reasonable sim-to-real transfer by deploying optimized trajectories in real-world settings. FluidLab is publicly available at: https://fluidlab2023.github.io.
|
https://openreview.net/pdf/6f396409f5100c7dca4d9a23810e4e4aefb8c5f2.pdf
|
Flow Matching for Generative Modeling
|
https://openreview.net/forum?id=PqvMRDCJT9t
|
https://openreview.net/forum?id=PqvMRDCJT9t
|
Yaron Lipman,Ricky T. Q. Chen,Heli Ben-Hamu,Maximilian Nickel,Matthew Le
|
ICLR 2023,Top 25%
|
We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability paths. Flow Matching is compatible with a general family of Gaussian probability paths for transforming between noise and data samples---which subsumes existing diffusion paths as specific instances. Interestingly, we find that employing FM with diffusion paths results in a more robust and stable alternative for training diffusion models. Furthermore, Flow Matching opens the door to training CNFs with other, non-diffusion probability paths. An instance of particular interest is using Optimal Transport (OT) displacement interpolation to define the conditional probability paths. These paths are more efficient than diffusion paths, provide faster training and sampling, and result in better generalization. Training CNFs using Flow Matching on ImageNet leads to consistently better performance than alternative diffusion-based methods in terms of both likelihood and sample quality, and allows fast and reliable sample generation using off-the-shelf numerical ODE solvers.
|
https://openreview.net/pdf/e99034416acd1ca82991f5d63735e77130fc06a7.pdf
|
PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
|
https://openreview.net/forum?id=tVkrbkz42vc
|
https://openreview.net/forum?id=tVkrbkz42vc
|
Xuan Li,Yi-Ling Qiao,Peter Yichen Chen,Krishna Murthy Jatavallabhula,Ming Lin,Chenfanfu Jiang,Chuang Gan
|
ICLR 2023,Top 25%
|
Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. This precludes their applicability in a vast majority of scenes where object geometries are complex or unknown. In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology. To this end, we propose "Physics Augmented Continuum Neural Radiance Fields" (PAC-NeRF), to estimate both the unknown geometry and physical parameters of highly dynamic objects from multi-view videos. We design PAC-NeRF to only ever produce physically plausible states by enforcing the neural radiance field to follow the conservation laws of continuum mechanics. For this, we design a hybrid Eulerian-Lagrangian representation of the neural radiance field, i.e., we use the Eulerian grid representation for NeRF density and color fields, while advecting the neural radiance fields via Lagrangian particles. This hybrid Eulerian-Lagrangian representation seamlessly blends efficient neural rendering with the material point method (MPM) for robust differentiable physics simulation. We validate the effectiveness of our proposed framework on geometry and physical parameter estimation over a vast range of materials, including elastic bodies, plasticine, sand, Newtonian and non-Newtonian fluids, and demonstrate significant performance gain on most tasks.
|
https://openreview.net/pdf/f216a90079436d1252e5157ce9e925639303e624.pdf
|
CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks
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https://openreview.net/forum?id=iPWiwWHc1V
|
https://openreview.net/forum?id=iPWiwWHc1V
|
Tuomas Oikarinen,Tsui-Wei Weng
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ICLR 2023,Top 25%
|
In this paper, we propose CLIP-Dissect, a new technique to automatically describe the function of individual hidden neurons inside vision networks. CLIP-Dissect leverages recent advances in multimodal vision/language models to label internal neurons with open-ended concepts without the need for any labeled data or human examples. We show that CLIP-Dissect provides more accurate descriptions than existing methods for last layer neurons where the ground-truth is available as well as qualitatively good descriptions for hidden layer neurons. In addition, our method is very flexible: it is model agnostic, can easily handle new concepts and can be extended to take advantage of better multimodal models in the future. Finally CLIP-Dissect is computationally efficient and can label all neurons from five layers of ResNet-50 in just 4 minutes, which is more than 10$\times$ faster than existing methods. Our code is available at https://github.com/Trustworthy-ML-Lab/CLIP-dissect.
|
https://openreview.net/pdf/a302e0072a6e15c8c0361c022bb9d3518f1a7127.pdf
|
Data Continuity Matters: Improving Sequence Modeling with Lipschitz Regularizer
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https://openreview.net/forum?id=27uBgHuoSQ
|
https://openreview.net/forum?id=27uBgHuoSQ
|
Eric Qu,Xufang Luo,Dongsheng Li
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ICLR 2023,Top 25%
|
Sequence modeling is a core problem in machine learning, and various neural networks have been designed to process different types of sequence data. However, few attempts have been made to understand the inherent data property of sequence data, neglecting the critical factor that may significantly affect the performance of sequence modeling. In this paper, we theoretically and empirically analyze a generic property of sequence data, i.e., continuity, and connect this property with the performance of deep models. First, we empirically observe that different kinds of models for sequence modeling prefer data with different continuity. Then, we theoretically analyze the continuity preference of different models in both time and frequency domains. To further utilize continuity to improve sequence modeling, we propose a simple yet effective Lipschitz Regularizer, that can flexibly adjust data continuity according to model preferences, and bring very little extra computational cost. Extensive experiments on various tasks demonstrate that altering data continuity via Lipschitz Regularizer can largely improve the performance of many deep models for sequence modeling.
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https://openreview.net/pdf/9377a798b9e647ef1515896beca548253de5e521.pdf
|
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
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https://openreview.net/forum?id=iaYcJKpY2B_
|
https://openreview.net/forum?id=iaYcJKpY2B_
|
Erik Nijkamp,Bo Pang,Hiroaki Hayashi,Lifu Tu,Huan Wang,Yingbo Zhou,Silvio Savarese,Caiming Xiong
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ICLR 2023,Top 25%
|
Program synthesis strives to generate a computer program as a solution to a given problem specification, expressed with input-output examples or natural language descriptions. The prevalence of large language models advances the state-of-the-art for program synthesis, though limited training resources and data impede open access to such models. To democratize this, we train and release a family of large language models up to 16.1B parameters, called CODEGEN, on natural language and programming language data, and open source the training library JAXFORMER. We show the utility of the trained model by demonstrating that it is competitive with the previous state-of-the-art on zero-shot Python code generation on HumanEval. We further investigate the multi-step paradigm for program synthesis, where a single program is factorized into multiple prompts specifying subproblems. To this end, we construct an open benchmark, Multi-Turn Programming Benchmark (MTPB), consisting of 115 diverse problem sets that are factorized into multi-turn prompts. Our analysis on MTPB shows that the same intent provided to CODEGEN in multi-turn fashion significantly improves program synthesis over that provided as a single turn. We make the training library JAXFORMER and model checkpoints available as open source contribution: https://github.com/salesforce/CodeGen.
|
https://openreview.net/pdf/003bbce081e6ee9edeead69fcdba6fbe3882de42.pdf
|
ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning
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https://openreview.net/forum?id=xYlJRpzZtsY
|
https://openreview.net/forum?id=xYlJRpzZtsY
|
Olga Golovneva,Moya Peng Chen,Spencer Poff,Martin Corredor,Luke Zettlemoyer,Maryam Fazel-Zarandi,Asli Celikyilmaz
|
ICLR 2023,Top 25%
|
Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness (independent of the final answer) is difficult without reliable methods for automatic evaluation. We simply do not know how often the stated reasoning steps actually support the final end task predictions. In this work, we present ROSCOE, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics. To evaluate ROSCOE against baseline metrics, we design a typology of reasoning errors and collect synthetic and human evaluation scores on commonly used reasoning datasets. In contrast with existing metrics, ROSCOE can measure semantic consistency, logicality, informativeness, fluency, and factuality — among other traits — by leveraging properties of step-by-step rationales. We empirically verify the strength of our metrics on five human annotated and six programmatically perturbed diagnostics datasets - covering a diverse set of tasks that require reasoning skills and show that ROSCOE can consistently outperform baseline metrics.
|
https://openreview.net/pdf/3f6164615b8f835462171508e65f188740d76ee8.pdf
|
Re-calibrating Feature Attributions for Model Interpretation
|
https://openreview.net/forum?id=WUWJIV2Yxtp
|
https://openreview.net/forum?id=WUWJIV2Yxtp
|
Peiyu Yang,NAVEED AKHTAR,Zeyi Wen,Mubarak Shah,Ajmal Saeed Mian
|
ICLR 2023,Top 25%
|
The ability to interpret machine learning models is critical for high-stakes applications. Due to its desirable theoretical properties, path integration is a widely used scheme for feature attribution to interpret model predictions. However, the methods implementing this scheme currently rely on absolute attribution scores to eventually provide sensible interpretations. This not only contradicts the premise that the features with larger attribution scores are more relevant to the model prediction, but also conflicts with the theoretical settings for which the desirable properties of the attributions are proven. We address this by devising a method to first compute an appropriate reference for the path integration scheme. This reference further helps in identifying valid interpolation points on a desired integration path. The reference is computed in a gradient ascending direction on the model's loss surface, while the interpolations are performed by analyzing the model gradients and variations between the reference and the input. The eventual integration is effectively performed along a non-linear path. Our scheme can be incorporated into the existing integral-based attribution methods. We also devise an effective sampling and integration procedure that enables employing our scheme with multi-reference path integration efficiently. We achieve a marked performance boost for a range of integral-based attribution methods on both local and global evaluation metrics by enhancing them with our scheme. Our extensive results also show improved sensitivity, sanity preservation and model robustness with the proposed re-calibration of the attribution techniques with our method.
|
https://openreview.net/pdf/27e850e1a146543993bd508afba29de6ac36bbdb.pdf
|
Adversarial Diversity in Hanabi
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https://openreview.net/forum?id=uLE3WF3-H_5
|
https://openreview.net/forum?id=uLE3WF3-H_5
|
Brandon Cui,Andrei Lupu,Samuel Sokota,Hengyuan Hu,David J Wu,Jakob Nicolaus Foerster
|
ICLR 2023,Top 25%
|
Many Dec-POMDPs admit a qualitatively diverse set of ''reasonable'' joint policies, where reasonableness is indicated by symmetry equivariance, non-sabotaging behaviour and the graceful degradation of performance when paired with ad-hoc partners. Some of the work in diversity literature is concerned with generating these policies. Unfortunately, existing methods fail to produce teams of agents that are simultaneously diverse, high performing, and reasonable. In this work, we propose a novel approach, adversarial diversity (ADVERSITY), which is designed for turn-based Dec-POMDPs with public actions. ADVERSITY relies on off-belief learning to encourage reasonableness and skill, and on ''repulsive'' fictitious transitions to encourage diversity. We use this approach to generate new agents with distinct but reasonable play styles for the card game Hanabi and open-source our agents to be used for future research on (ad-hoc) coordination.
|
https://openreview.net/pdf/87e565d42543f1efac5413ce0bdc2276e4f99253.pdf
|
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