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2,900
|
FlexiClip: Locality-Preserving Free-Form Character Animation
|
https://openreview.net/forum?id=xtxCM4XZ82
|
[
"Anant Khandelwal"
] |
Poster
|
Animating clipart images with seamless motion while maintaining visual fidelity and temporal coherence presents significant challenges. Existing methods, such as AniClipart, effectively model spatial deformations but often fail to ensure smooth temporal transitions, resulting in artifacts like abrupt motions and geometric distortions. Similarly, text-to-video (T2V) and image-to-video (I2V) models struggle to handle clipart due to the mismatch in statistical properties between natural video and clipart styles. This paper introduces FlexiClip, a novel approach designed to overcome these limitations by addressing the intertwined challenges of temporal consistency and geometric integrity. FlexiClip extends traditional Bézier curve-based trajectory modeling with key innovations: temporal Jacobians to correct motion dynamics incrementally, continuous-time modeling via probability flow ODEs (pfODEs) to mitigate temporal noise, and a flow matching loss inspired by GFlowNet principles to optimize smooth motion transitions. These enhancements ensure coherent animations across complex scenarios involving rapid movements and non-rigid deformations. Extensive experiments validate the effectiveness of FlexiClip in generating animations that are not only smooth and natural but also structurally consistent across diverse clipart types, including humans and animals. By integrating spatial and temporal modeling with pre-trained video diffusion models, FlexiClip sets a new standard for high-quality clipart animation, offering robust performance across a wide range of visual content. Project Page: https://creative-gen.github.io/flexiclip.github.io/
|
Text-to-Video generation
|
clipart animation, temporal Jacobians, probability flow ODEs, flow matching loss, GFlowNet, continuous-time modeling, character animation
| 909
|
2501.08676
|
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] | null |
2,901
|
FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training
|
https://openreview.net/forum?id=B4TyAILcE4
|
[
"Philip Zmushko",
"Aleksandr Beznosikov",
"Martin Takáč",
"Samuel Horváth"
] |
Poster
|
With the increase in the number of parameters in large language models, the training process increasingly demands larger volumes of GPU memory. A significant portion of this memory is typically consumed by the optimizer state. To overcome this challenge, recent approaches such as low-rank adaptation (LoRA), low-rank gradient projection (GaLore), and blockwise optimization (BAdam) have been proposed.
However, in all these algorithms, the *effective rank of the weight updates remains low-rank*, which can lead to a substantial loss of information from the gradient. This loss can be critically important, especially during the pre-training stage. In this paper, we introduce FRUGAL (**F**ull-**R**ank **U**pdates with **G**r**A**dient sp**L**itting), a new memory-efficient optimization framework. FRUGAL leverages gradient splitting to perform low-dimensional updates using advanced algorithms (such as Adam), while updates along the remaining directions are executed via state-free methods like SGD or signSGD. Our framework can be integrated with various low-rank update selection techniques, including GaLore and BAdam. We provide theoretical convergence guarantees for our framework when using SGDM for low-dimensional updates and SGD for state-free updates. Additionally, our method consistently outperforms concurrent approaches, achieving state-of-the-art results in pre-training and fine-tuning tasks while balancing memory efficiency and performance metrics.
|
Memory-efficient training, Optimization, Full-Rank Update, Large Language Models, LLM, Pre-training, Fine-tuning
|
We create a memory-efficient optimization framework that performs full-rank updates by combining advanced methods like Adam with state-free methods like signSGD.
| 908
|
2411.07837
|
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] |
https://github.com/fzmushko/frugal
|
2,902
|
Enhancing Statistical Validity and Power in Hybrid Controlled Trials: A Randomization Inference Approach with Conformal Selective Borrowing
|
https://openreview.net/forum?id=WvanLeuEAC
|
[
"Ke Zhu",
"Shu Yang",
"Xiaofei Wang"
] |
Poster
|
External controls from historical trials or observational data can augment randomized controlled trials when large-scale randomization is impractical or unethical, such as in drug evaluation for rare diseases. However, non-randomized external controls can introduce biases, and existing Bayesian and frequentist methods may inflate the type I error rate, particularly in small-sample trials where external data borrowing is most critical. To address these challenges, we propose a randomization inference framework that ensures finite-sample exact and model-free type I error rate control, adhering to the “analyze as you randomize” principle to safeguard against hidden biases. Recognizing that biased external controls reduce the power of randomization tests, we leverage conformal inference to develop an individualized test-then-pool procedure that selectively borrows comparable external controls to improve power. Our approach incorporates selection uncertainty into randomization tests, providing valid post-selection inference. Additionally, we propose an adaptive procedure to optimize the selection threshold by minimizing the mean squared error across a class of estimators encompassing both no-borrowing and full-borrowing approaches. The proposed methods are supported by non-asymptotic theoretical analysis, validated through simulations, and applied to a randomized lung cancer trial that integrates external controls from the National Cancer Database.
|
causal inference, data fusion, randomization test, real-world data and evidence, small sample size
|
This paper proposes a randomization inference framework using conformal inference to selectively borrow external controls, ensuring exact type I error control and valid post-selection inference, with application to a lung cancer trial.
| 907
|
2410.11713
|
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] |
https://github.com/ke-zhu/intFRT
|
2,903
|
ENSUR: Equitable and Statistically Unbiased Recommendation
|
https://openreview.net/forum?id=iLm1cNnjzk
|
[
"Nitin Bisht",
"Xiuwen Gong",
"Guandong Xu"
] |
Poster
|
Although Recommender Systems (RS) have been well-developed for various fields of applications, they often suffer from a crisis of platform credibility with respect to RS confidence and fairness, which may drive users away, threatening the platform's long-term success. In recent years, some works have tried to solve these issues; however, they lack strong statistical guarantees. Therefore, there is an urgent need to solve both issues with a unifying framework with robust statistical guarantees. In this paper, we propose a novel and reliable framework called Equitable and Statistically Unbiased Recommendation (ENSUR)) to dynamically generate prediction sets for users across various groups, which are guaranteed 1) to include ground-truth items with user-predefined high confidence/probability (e.g., 90\%); 2) to ensure user fairness across different groups; 3) to have minimum efficient average prediction set sizes.
We further design an efficient algorithm named Guaranteed User Fairness Algorithm (GUFA) to optimize the proposed method and derive upper bounds of risk and fairness metrics to speed up the optimization process.
Moreover, we provide rigorous theoretical analysis concerning risk and fairness control and minimum set size. Extensive experiments validate the effectiveness of the proposed framework, which aligns with our theoretical analysis.
|
Dynamic Prediction Sets
| null | 904
| null |
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] | null |
2,904
|
COKE: Core Kernel for More Efficient Approximation of Kernel Weights in Multiple Kernel Clustering
|
https://openreview.net/forum?id=0BsEMZDzWr
|
[
"Weixuan Liang",
"Xinwang Liu",
"KE LIANG",
"Jiyuan Liu",
"En Zhu"
] |
Poster
|
Inspired by the well-known coreset in clustering algorithms, we introduce the definition of the core kernel for multiple kernel clustering (MKC) algorithms. The core kernel refers to running MKC algorithms on smaller-scale base kernel matrices to obtain kernel weights similar to those obtained from the original full-scale kernel matrices. Specifically, the core kernel refers to a set of kernel matrices of size $\widetilde{\mathcal{O}}(1/\varepsilon^2)$ that perform MKC algorithms on them can achieve a $(1+\varepsilon)$-approximation for the kernel weights. Subsequently, we can leverage approximated kernel weights to obtain a theoretically guaranteed large-scale extension of MKC algorithms. In this paper, we propose a core kernel construction method based on singular value decomposition and prove that it satisfies the definition of the core kernel for three mainstream MKC algorithms. Finally, we conduct experiments on several benchmark datasets to verify the correctness of theoretical results and the efficiency of the proposed method.
|
Multiple Kernel Clustering, Multi-View Clustering
| null | 892
| null |
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] | null |
2,905
|
Bifurcate then Alienate: Incomplete Multi-view Clustering via Coupled Distribution Learning with Linear Overhead
|
https://openreview.net/forum?id=EFMAjY8dVZ
|
[
"Shengju Yu",
"Yiu-ming Cheung",
"Siwei Wang",
"Xinwang Liu",
"En Zhu"
] |
Poster
|
Despite remarkable advances, existing incomplete multi-view clustering (IMC) methods typically leverage either perspective-shared or perspective-specific determinants to encode cluster representations. To address this limitation, we introduce a BACDL algorithm designed to explicitly capture both concurrently, thereby exploiting heterogeneous data more effectively. It chooses to bifurcate feature clusters and further alienate them to enlarge the discrimination. With distribution learning, it successfully couples view guidance into feature clusters to alleviate dimension inconsistency. Then, building on the principle that samples in one common cluster own similar marginal distribution and conditional distribution, it unifies the association between feature clusters and sample clusters to bridge all views. Thereafter, all incomplete sample clusters are reordered and mapped to a common one to formulate clustering embedding. Last, the overall linear overhead endows it with a resource-efficient characteristic.
|
Incomplete Mulit-view Clustering
| null | 888
| null |
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] | null |
2,906
|
Large Language Models to Diffusion Finetuning
|
https://openreview.net/forum?id=6WBca6adox
|
[
"Edoardo Cetin",
"Tianyu Zhao",
"Yujin Tang"
] |
Poster
|
We propose a new finetuning method to provide pre-trained large language models (LMs) the ability to scale test-time compute through the diffusion framework. By increasing the number of diffusion steps, we show our finetuned models achieve monotonically increasing accuracy, directly translating to improved performance across downstream tasks. Furthermore, our finetuned models can expertly answer questions on specific topics by integrating powerful guidance techniques, and autonomously determine the compute required for a given problem by leveraging adaptive ODE solvers. Our method is applicable to any foundation model pre-trained with cross-entropy and does not modify any of its original weights, fully preserving its strong single-step generation capabilities. We show our method can be more effective and is fully compatible with traditional finetuning and search approaches, introducing an orthogonal new direction to unify the strengths of the autoregressive and diffusion frameworks.
|
language models, large language models, diffusion, finetuning, test-time scaling
|
New finetuning method powering large language models with some of the key properties of diffusion models and the ability to scale test-time compute.
| 879
|
2501.15781
|
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] |
https://github.com/SakanaAI/L2D
|
2,907
|
Learning Event Completeness for Weakly Supervised Video Anomaly Detection
|
https://openreview.net/forum?id=JaNKGPkDpw
|
[
"Yu Wang",
"Shiwei Chen"
] |
Poster
|
Weakly supervised video anomaly detection (WS-VAD) is tasked with pinpointing temporal intervals containing anomalous events within untrimmed videos, utilizing only video-level annotations. However, a significant challenge arises due to the absence of dense frame-level annotations, often leading to incomplete localization in existing WS-VAD methods. To address this issue, we present a novel LEC-VAD, Learning Event Completeness for Weakly Supervised Video Anomaly Detection, which features a dual structure designed to encode both category-aware and category-agnostic semantics between vision and language. Within LEC-VAD, we devise semantic regularities that leverage an anomaly-aware Gaussian mixture to learn precise event boundaries, thereby yielding more complete event instances. Besides, we develop a novel memory bank-based prototype learning mechanism to enrich concise text descriptions associated with anomaly-event categories. This innovation bolsters the text's expressiveness, which is crucial for advancing WS-VAD. Our LEC-VAD demonstrates remarkable advancements over the current state-of-the-art methods on two benchmark datasets XD-Violence and UCF-Crime.
|
Weakly-supervised video anomaly detection, multi-modal video understanding
|
A novel dual-structed framework is proposed to learn event completeness for weak-supervised video anomaly detection
| 873
|
2506.13095
|
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] | null |
2,908
|
SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation
|
https://openreview.net/forum?id=aTyQjpsaB4
|
[
"Zihan Liu",
"Shuangrui Ding",
"Zhixiong Zhang",
"Xiaoyi Dong",
"Pan Zhang",
"Yuhang Zang",
"Yuhang Cao",
"Dahua Lin",
"Jiaqi Wang"
] |
Poster
|
Text-to-song generation, the task of creating vocals and accompaniment from textual inputs, poses significant challenges due to domain complexity and data scarcity.
Existing approaches often employ multi-stage generation procedures, leading to cumbersome training and inference pipelines, as well as suboptimal overall generation quality due to error accumulation across stages.
In this paper, we propose **SongGen**, a fully open-source, single-stage auto-regressive transformer designed for controllable song generation. The proposed model facilitates fine-grained control over diverse musical attributes, including lyrics and textual descriptions of instrumentation, genre, mood, and timbre, while also offering an optional three-second reference clip for voice cloning. Within a unified auto-regressive framework, SongGen supports two output modes: **mixed mode**, which generates a mixture of vocals and accompaniment directly, and **dual-track mode**, which synthesizes them separately for greater flexibility in downstream applications. We explore diverse token pattern strategies for each mode, leading to notable improvements and valuable insights. Furthermore, we design an automated data preprocessing pipeline with effective quality control. To foster community engagement and future research, we will release our model weights, training code, annotated data, and preprocessing pipeline.
The code is available at https://github.com/LiuZH-19/SongGen.
|
text-to-song, song generation, auto-regressive transformer
|
We propose SonGen, a fully open-source, single-stage auto-regressive transformer designed for controllable song generation, supporting both mixed mode and dual-track mode generation.
| 866
|
2502.13128
|
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] |
https://github.com/LiuZH-19/SongGen
|
2,909
|
BDC-CLIP: Brownian Distance Covariance for Adapting CLIP to Action Recognition
|
https://openreview.net/forum?id=fjXcRSfyIV
|
[
"Fei Long",
"Xiaoou Li",
"Jiaming Lv",
"Haoyuan Yang",
"Xianjun Cheng",
"Peihua Li"
] |
Poster
|
Bridging contrastive language-image pre-training (CLIP) to video action recognition has attracted growing interest. Human actions are inherently rich in spatial and temporal contexts, involving dynamic interactions among people, objects, and the environment. Accurately recognizing actions requires effectively capturing these fine-grained elements and modeling their relationships with language. However, most existing methods rely on cosine similarity--practically equivalent to the Pearson correlation coefficient--between global tokens for video-language alignment. As a result, they have limited capacity to model complex dependencies and tend to overlook local tokens that encode critical spatio-temporal cues. To overcome these limitations, we propose BDC-CLIP, a novel framework that leverages Brownian Distance Covariance (BDC) to align visual and textual representations. Our method can capture complex relationships--both linear and nonlinear--between all visual and textual tokens, enabling fine-grained modeling in space, time, and language. BDC-CLIP achieves state-of-the-art performance across zero-shot, few-shot, base-to-novel, and fully supervised action recognition settings, demonstrating its effectiveness and broad applicability.
|
Action recognition, video-language alignment, Brownian distance covariance, CLIP
|
We adapt CLIP for action recognition based on Brownian distance covariance (BDC) using all visual and textual tokens. It addresses limitations of previous arts relying on cosine similarity, highlighting significance of advanced metrics like BDC.
| 859
| null |
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] | null |
2,910
|
Provably Efficient Exploration in Inverse Constrained Reinforcement Learning
|
https://openreview.net/forum?id=eLTPkGGHum
|
[
"Bo Yue",
"Jian Li",
"Guiliang Liu"
] |
Poster
|
Optimizing objective functions subject to constraints is fundamental in many real-world applications. However, these constraints are often not readily defined and must be inferred from expert agent behaviors, a problem known as Inverse Constraint Inference. Inverse Constrained Reinforcement Learning (ICRL) is a common solver for recovering feasible constraints in complex environments, relying on training samples collected from interactive environments. However, the efficacy and efficiency of current sampling strategies remain unclear. We propose a strategic exploration framework for sampling with guaranteed efficiency to bridge this gap. By defining the feasible cost set for ICRL problems, we analyze how estimation errors in transition dynamics and the expert policy influence the feasibility of inferred constraints. Based on this analysis, we introduce two exploratory algorithms to achieve efficient constraint inference via 1) dynamically reducing the bounded aggregate error of cost estimations or 2) strategically constraining the exploration policy around plausibly optimal ones. Both algorithms are theoretically grounded with tractable sample complexity, and their performance is validated empirically across various environments.
|
Inverse Constrained Reinforcement Learning, Exploration Algorithm, Sample Efficiency
|
This paper introduces a strategically efficient exploration framework for Inverse Constrained Reinforcement Learning problems with theoretically tractable sample complexity.
| 855
|
2409.15963
|
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] | null |
2,911
|
EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions
|
https://openreview.net/forum?id=ZZvTc92dYQ
|
[
"Huayu Deng",
"Xiangming Zhu",
"Yunbo Wang",
"Xiaokang Yang"
] |
Poster
|
Graph neural networks have been a powerful tool for mesh-based physical simulation. To efficiently model large-scale systems, existing methods mainly employ hierarchical graph structures to capture multi-scale node relations. However, these graph hierarchies are typically manually designed and fixed, limiting their ability to adapt to the evolving dynamics of complex physical systems. We propose EvoMesh, a fully differentiable framework that jointly learns graph hierarchies and physical dynamics, adaptively guided by physical inputs. EvoMesh introduces anisotropic message passing, which enables direction-specific aggregation of dynamic features between nodes within each hierarchy, while simultaneously learning node selection probabilities for the next hierarchical level based on physical context. This design creates more flexible message shortcuts and enhances the model's capacity to capture long-range dependencies. Extensive experiments on five benchmark physical simulation datasets show that EvoMesh outperforms recent fixed-hierarchy message passing networks by large margins. The project page is available at https://hbell99.github.io/evo-mesh/.
|
Physical Simulation, Message Passing Networks
|
EvoMesh is a fully differentiable framework that jointly learns graph hierarchies and physical dynamics.
| 852
|
2410.03779
|
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] |
https://github.com/hbell99/evomesh
|
2,912
|
Gaussian Mixture Flow Matching Models
|
https://openreview.net/forum?id=CoDoOe6Xab
|
[
"Hansheng Chen",
"Kai Zhang",
"Hao Tan",
"Zexiang Xu",
"Fujun Luan",
"Leonidas Guibas",
"Gordon Wetzstein",
"Sai Bi"
] |
Poster
|
Diffusion models approximate the denoising distribution as a Gaussian and predict its mean, whereas flow matching models reparameterize the Gaussian mean as flow velocity. However, they underperform in few-step sampling due to discretization error and tend to produce over-saturated colors under classifier-free guidance (CFG). To address these limitations, we propose a novel Gaussian mixture flow matching (GMFlow) model: instead of predicting the mean, GMFlow predicts dynamic Gaussian mixture (GM) parameters to capture a multi-modal flow velocity distribution, which can be learned with a KL divergence loss. We demonstrate that GMFlow generalizes previous diffusion and flow matching models where a single Gaussian is learned with an $L_2$ denoising loss. For inference, we derive GM-SDE/ODE solvers that leverage analytic denoising distributions and velocity fields for precise few-step sampling. Furthermore, we introduce a novel probabilistic guidance scheme that mitigates the over-saturation issues of CFG and improves image generation quality. Extensive experiments demonstrate that GMFlow consistently outperforms flow matching baselines in generation quality, achieving a Precision of 0.942 with only 6 sampling steps on ImageNet 256$\times$256.
|
GMFlow, diffusion models, flow matching, Gaussian mixture
|
GMFlow generalizes diffusion models by predicting Gaussian mixture denoising distributions, enabling precise few-step sampling and high-quality generation.
| 847
|
2504.05304
|
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] |
https://github.com/Lakonik/GMFlow
|
2,913
|
Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions
|
https://openreview.net/forum?id=iT8qIzNSAl
|
[
"Dongze Wu",
"Yao Xie"
] |
Poster
|
Sampling from high-dimensional, multi-modal distributions remains a fundamental challenge across domains such as statistical Bayesian inference and physics-based machine learning. In this paper, we propose Annealing Flow (AF), a method built on Continuous Normalizing Flows (CNFs) for sampling from high-dimensional and multi-modal distributions. AF is trained with a dynamic Optimal Transport (OT) objective incorporating Wasserstein regularization, and guided by annealing procedures, facilitating effective exploration of modes in high-dimensional spaces. Compared to recent NF methods, AF significantly improves training efficiency and stability, with minimal reliance on MC assistance. We demonstrate the superior performance of AF compared to state-of-the-art methods through extensive experiments on various challenging distributions and real-world datasets, particularly in high-dimensional and multi-modal settings. We also highlight AF’s potential for sampling the least favorable distributions.
|
Statistical Sampling, Optimal Transport, Continuous Normalizing Flows
| null | 846
|
2409.20547
|
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] |
https://github.com/StatFusion/Annealing-Flow-For-Sampling
|
2,914
|
In-Context Learning as Conditioned Associative Memory Retrieval
|
https://openreview.net/forum?id=Zup6F3MwQO
|
[
"Weimin Wu",
"Teng-Yun Hsiao",
"Jerry Yao-Chieh Hu",
"Wenxin Zhang",
"Han Liu"
] |
Poster
|
We provide an exactly solvable example for interpreting In-Context Learning (ICL) with one-layer attention models as conditional retrieval of dense associative memory models.
Our main contribution is to interpret ICL as memory reshaping in the modern Hopfield model from a conditional memory set (in-context examples).
Specifically, we show that the in-context sequential examples induce an effective reshaping of the energy landscape of a Hopfield model.
We integrate this in-context memory reshaping phenomenon into the existing Bayesian model averaging view of ICL [Zhang et al., AISTATS 2025] via the established equivalence between the modern Hopfield model and transformer attention.
Under this unique perspective, we not only characterize how in-context examples shape predictions in the Gaussian linear regression case, but also recover the known $\epsilon$-stability generalization bound of the ICL for the one-layer attention model.
We also give explanations for three key behaviors of ICL and validate them through experiments.
|
In-Context Learning, Large Language Model, Foundation Model, Transformer, Attention, Modern Hopfield Model, Associative Memory
|
We provide an exactly solvable example for interpreting In-Context Learning (ICL) with one-layer attention models as conditional retrieval of dense associative memory models.
| 839
| null |
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] | null |
2,915
|
Nonparametric Modern Hopfield Models
|
https://openreview.net/forum?id=xkV3uCQtJm
|
[
"Jerry Yao-Chieh Hu",
"Bo-Yu Chen",
"Dennis Wu",
"Feng Ruan",
"Han Liu"
] |
Poster
|
We present a nonparametric interpretation for deep learning compatible modern Hopfield models and utilize this new perspective to debut efficient variants.
Our key contribution stems from interpreting the memory storage and retrieval processes in modern Hopfield models as a nonparametric regression problem subject to a set of query-memory pairs.
Interestingly,
our framework not only recovers the known results from the original dense modern Hopfield model but also fills the void in the literature regarding efficient modern Hopfield models, by introducing *sparse-structured* modern Hopfield models with sub-quadratic complexity.
We establish that this sparse model inherits the appealing theoretical properties of its dense analogue --- connection with transformer attention, fixed point convergence and exponential memory capacity.
Additionally, we showcase the versatility of our framework by constructing a family of modern Hopfield models as extensions, including linear, random masked, top-$K$ and positive random feature modern Hopfield models.
Empirically, we validate our framework in both synthetic and realistic settings for memory retrieval and learning tasks.
|
dense associative memory, modern Hopfield model, Hopfield network, efficient transformer, efficient attention, transformer, attention, foundation model, large language model
|
A nonparametric construction for transformer-compatible modern Hopfield models and utilize this framework to debut efficient variants.
| 836
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2404.03900
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] |
https://github.com/MAGICS-LAB/NonparametricHopfield
|
2,916
|
Fast and Low-Cost Genomic Foundation Models via Outlier Removal
|
https://openreview.net/forum?id=oyUiJmkD7H
|
[
"Haozheng Luo",
"Chenghao Qiu",
"Maojiang Su",
"Zhihan Zhou",
"Zoe Mehta",
"Guo Ye",
"Jerry Yao-Chieh Hu",
"Han Liu"
] |
Poster
|
To address the challenge of scarce computational resources in genomic modeling, we introduce GERM, a genomic foundation model optimized for accessibility and adaptability. GERM improves upon models like DNABERT-2 by eliminating outliers that hinder low-rank adaptation and post-training quantization, enhancing both efficiency and robustness. We replace the vanilla attention layer with an outlier-free mechanism inspired by associative memory models. By removing outliers during both pre-training and fine-tuning, this approach accelerates adaptation, reduces computational costs, and enhances quantization robustness within acceptable loss margins. Additionally, we propose GERM-T, a strategy that employs small-step continual learning within the outlier-free framework, leveraging original checkpoints to avoid retraining from scratch. Empirically, GERM improves fine-tuning performance by 37.98% and quantization by 64.34% over the baseline model. It also reduces average kurtosis by 92.14% and maximum infinity norm by 82.77%. Compared to leading methods, GERM consistently delivers superior performance, offering a practical solution for genomic modeling in resource-constrained settings.
|
DNA, Outlier-free, Genomic Foundation Models, Outlier Removal
|
GERM is a genomic foundation model optimized for low-resource settings by removing outliers, enhancing low-rank adaptation and quantization, achieving up to 64.34% efficiency gains and 37.98% better fine-tuning performance over baseline models.
| 832
|
2505.00598
|
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] |
https://github.com/MAGICS-LAB/GERM
|
2,917
|
Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion
|
https://openreview.net/forum?id=oRdfFS7xO5
|
[
"Tianyuan Zou",
"Yang Liu",
"Peng Li",
"Yufei Xiong",
"Jianqing Zhang",
"Jingjing Liu",
"Xiaozhou Ye",
"Ye Ouyang",
"Ya-Qin Zhang"
] |
Poster
|
Substantial quantity and high quality are the golden rules of making a good training dataset with sample privacy protection equally important. Generating synthetic samples that resemble high-quality private data while ensuring Differential Privacy (DP), a formal privacy guarantee, promises scalability and practicality. However, existing methods relying on pre-trained models for data synthesis often struggle in data-deficient scenarios, suffering from limited sample size, inevitable generation noise and existing pre-trained model bias. To address these challenges, we propose a novel contr**A**stive private data **S**ynthesis via **W**eighted multiple **P**re-trained generative models framework, named as **WASP**. WASP utilizes limited private samples for more accurate private data distribution estimation via a Top-*Q* voting mechanism, and leverages low-quality synthetic samples for contrastive generation via collaboration among dynamically weighted multiple pre-trained models. Extensive experiments on 6 well-developed datasets with 6 open-source and 3 closed-source PLMs demonstrate the superiority of WASP in improving model performance over diverse downstream tasks. Code is available at https://github.com/LindaLydia/WASP.
|
Differentially Private Synthetic Dataset, Collaboration between Private Data and Private Model, Fusion of Pre-trained Language Model
| null | 830
|
2502.00245
|
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] |
https://github.com/LindaLydia/WASP
|
2,918
|
Parameter-Efficient Fine-Tuning of State Space Models
|
https://openreview.net/forum?id=64mHSb9DlQ
|
[
"Kevin Galim",
"Wonjun Kang",
"Yuchen Zeng",
"Hyung Il Koo",
"Kangwook Lee"
] |
Poster
|
Deep State Space Models (SSMs), such as Mamba (Gu & Dao, 2024), have become powerful tools for language modeling, offering high performance and linear scalability with sequence length. However, the application of parameter-efficient fine-tuning (PEFT) methods to SSM-based models remains largely underexplored. We start by investigating two fundamental questions on existing PEFT methods: (i) How do they perform on SSM-based models? (ii) Which parameters should they target for optimal results? Our analysis shows that LoRA and its variants consistently outperform all other PEFT methods. While LoRA is effective for linear projection matrices, it fails on SSM modules—yet still outperforms other methods applicable to SSMs, indicating their limitations. This underscores the need for a specialized SSM tuning approach. To address this, we propose Sparse Dimension Tuning (SDT), a PEFT method tailored for SSM modules. Combining SDT for SSMs with LoRA for linear projection matrices, we achieve state-of-the-art performance across extensive experiments.
|
parameter-efficient fine-tuning, state space model, mamba, lora
| null | 822
|
2410.09016
|
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] |
https://github.com/furiosa-ai/ssm-peft
|
2,919
|
Deterministic Sparse Fourier Transform for Continuous Signals with Frequency Gap
|
https://openreview.net/forum?id=enwylUTeuP
|
[
"Xiaoyu Li",
"Zhao Song",
"Shenghao Xie"
] |
Poster
|
The Fourier transform is a fundamental tool in computer science and signal processing. In particular, when the signal is sparse in the frequency domain---having only $k$ distinct frequencies---sparse Fourier transform (SFT) algorithms can recover the signal in a sublinear time (proportional to the sparsity $k$). Most prior research focused on SFT for discrete signals, designing both randomized and deterministic algorithms for one-dimensional and high-dimensional discrete signals. However, SFT for continuous signals (i.e., $x^*(t)=\sum_{j=1}^k v_j e^{2\pi \mathbf{i} f_j t}$ for $t\in [0,T]$) is a more challenging task. The discrete SFT algorithms are not directly applicable to continuous signals due to the sparsity blow-up from the discretization. Prior to this work, there is a randomized algorithm that achieves an $\ell_2$ recovery guarantee in $\widetilde{O}(k\cdot \mathrm{polylog}(F/\eta))$ time, where $F$ is the band-limit of the frequencies and $\eta$ is the frequency gap.
Nevertheless, whether we can solve this problem without using randomness remains open. In this work, we address this gap and introduce
the first sublinear-time deterministic sparse Fourier transform algorithm in the continuous setting. Specifically, our algorithm uses $\widetilde{O}(k^2 \cdot \mathrm{polylog}(F/\eta))$ samples and $\widetilde{O}(k^2 \cdot \mathrm{polylog}(F/\eta))$ time to reconstruct the on-grid signal with arbitrary noise that satisfies a mild condition. This is the optimal recovery guarantee that can be achieved by any deterministic approach.
|
Fourier transform, sparse recovery, derandomization, sublinear time
| null | 819
| null |
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] | null |
2,920
|
Structure-informed Risk Minimization for Robust Ensemble Learning
|
https://openreview.net/forum?id=GvV9MJs268
|
[
"Fengchun Qiao",
"Yanlin Chen",
"Xi Peng"
] |
Poster
|
Ensemble learning is a powerful approach for improving generalization under distribution shifts, but its effectiveness heavily depends on how individual models are combined. Existing methods often optimize ensemble weights based on validation data, which may not represent unseen test distributions, leading to suboptimal performance in out-of-distribution (OoD) settings. Inspired by Distributionally Robust Optimization (DRO), we propose Structure-informed Risk Minimization (SRM), a principled framework that learns robust ensemble weights without access to test data. Unlike standard DRO, which defines uncertainty sets based on divergence metrics alone, SRM incorporates structural information of training distributions, ensuring that the uncertainty set aligns with plausible real-world shifts. This approach mitigates the over-pessimism of traditional worst-case optimization while maintaining robustness. We introduce a computationally efficient optimization algorithm with theoretical guarantees and demonstrate that SRM achieves superior OoD generalization compared to existing ensemble combination strategies across diverse benchmarks. Code is available at: https://github.com/deep-real/SRM.
|
Out-of-distribution Generalization
|
We leverage distributional graph to learn robust ensemble weights for out-of-distribution generalization.
| 814
| null |
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] |
https://github.com/deep-real/SRM
|
2,921
|
LETS Forecast: Learning Embedology for Time Series Forecasting
|
https://openreview.net/forum?id=LLk1qYQatJ
|
[
"Abrar Majeedi",
"Viswanatha Reddy Gajjala",
"Satya Sai Srinath Namburi GNVV",
"Nada Magdi Elkordi",
"Yin Li"
] |
Poster
|
Real-world time series are often governed by complex nonlinear dynamics. Understanding these underlying dynamics is crucial for precise future prediction. While deep learning has achieved major success in time series forecasting, many existing approaches do not explicitly model the dynamics. To bridge this gap, we introduce DeepEDM, a framework that integrates nonlinear dynamical systems modeling with deep neural networks. Inspired by empirical dynamic modeling (EDM) and rooted in Takens' theorem, DeepEDM presents a novel deep model that learns a latent space from time-delayed embeddings, and employs kernel regression to approximate the underlying dynamics, while leveraging efficient implementation of softmax attention and allowing for accurate prediction of future time steps. To evaluate our method, we conduct comprehensive experiments on synthetic data of nonlinear dynamical systems as well as real-world time series across domains. Our results show that DeepEDM is robust to input noise, and outperforms state-of-the-art methods in forecasting accuracy. Our code is available at: https://abrarmajeedi.github.io/deep_edm.
|
Time series forecasting, dynamical modeling
|
DeepEDM integrates nonlinear dynamical systems modeling with deep learning by using time-delayed embeddings and kernel regression, significantly improving time series forecasting accuracy over state-of-the-art methods.
| 812
|
2506.06454
|
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] | null |
2,922
|
Customizing the Inductive Biases of Softmax Attention using Structured Matrices
|
https://openreview.net/forum?id=Roc5O1ECEt
|
[
"Yilun Kuang",
"Noah Amsel",
"Sanae Lotfi",
"Shikai Qiu",
"Andres Potapczynski",
"Andrew Gordon Wilson"
] |
Poster
|
The core component of attention is the scoring function, which transforms the inputs into low-dimensional queries and keys and takes the dot product of each pair. While the low-dimensional projection improves efficiency, it causes information loss for certain tasks that have intrinsically high-dimensional inputs. Additionally, attention uses the same scoring function for all input pairs, without imposing a distance-dependent compute bias for neighboring tokens in the sequence. In this work, we address these shortcomings by proposing new scoring functions based on computationally efficient structured matrices with high ranks, including Block Tensor-Train (BTT) and Multi-Level Low Rank (MLR) matrices. On in-context regression tasks with high-dimensional inputs, our proposed scoring functions outperform standard attention for any fixed compute budget. On language modeling, a task that exhibits locality patterns, our MLR-based attention method achieves improved scaling laws compared to both standard attention and variants of sliding window attention.
Additionally, we show that both BTT and MLR fall under a broader family of efficient structured matrices capable of encoding either full-rank or distance-dependent compute biases, thereby addressing significant shortcomings of standard attention.
|
Attention, Inductive Biases, Low Rank Bottleneck, Locality Bias, Hierarchical Matrices, Structured Matrices, Language Modeling, In-Context Regression
|
We use specialized structured matrices instead of low rank ones to compute the attention scores. This helps improve two shortcomings of standard attention for certain tasks.
| 809
| null |
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] |
https://github.com/YilunKuang/structured-attention
|
2,923
|
Competing Bandits in Matching Markets via Super Stability
|
https://openreview.net/forum?id=mNxjoMiFFf
|
[
"Soumya Basu"
] |
Poster
|
We study bandit learning in matching markets with two-sided reward uncertainty, extending prior research primarily focused on single-sided uncertainty. Leveraging the concept of `super-stability' from Irving (1994), we demonstrate the advantage of the Extended Gale-Shapley (GS) algorithm over the standard GS algorithm in achieving true stable matchings under incomplete information. By employing the Extended GS algorithm, our centralized algorithm attains a logarithmic pessimal stable regret dependent on an instance-dependent admissible gap parameter. This algorithm is further adapted to a decentralized setting with a constant regret increase. Finally, we establish a novel centralized instance-dependent lower bound for binary stable regret, elucidating the roles of the admissible gap and super-stable matching in characterizing the complexity of stable matching with bandit feedback.
|
Bandits, Matching Markets, Stable Matching, Super Stability
|
Extended Gale-Shapley algorithm with exploration finds stable matchings in bandit learning with two-sided reward uncertainty.
| 808
|
2506.15926
|
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] | null |
2,924
|
Safety Alignment Can Be Not Superficial With Explicit Safety Signals
|
https://openreview.net/forum?id=e9YosoGkYg
|
[
"Jianwei Li",
"Jung-Eun Kim"
] |
Poster
|
Recent studies on the safety alignment of large language models (LLMs) have revealed that existing approaches often operate superficially, leaving models vulnerable to various adversarial attacks. Despite their significance, these studies generally fail to offer actionable solutions beyond data augmentation for achieving more robust safety mechanisms. This paper identifies a fundamental cause of this superficiality: existing alignment approaches often presume that models can implicitly learn a safety-related reasoning task during the alignment process, enabling them to refuse harmful requests. However, the learned safety signals are often diluted by other competing objectives, leading models to struggle with drawing a firm safety-conscious decision boundary when confronted with adversarial attacks. Based on this observation, by explicitly introducing a safety-related binary classification task and integrating its signals with our attention and decoding strategies, we eliminate this ambiguity and allow models to respond more responsibly to malicious queries. We emphasize that, with less than 0.2x overhead cost, our approach enables LLMs to assess the safety of both the query and the previously generated tokens at each necessary generating step. Extensive experiments demonstrate that our method significantly improves the resilience of LLMs against various adversarial attacks, offering a promising pathway toward more robust generative AI systems.
|
Large Language Model, LLM, Safety Alignment
| null | 807
|
2505.17072
|
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] | null |
2,925
|
A Rescaling-Invariant Lipschitz Bound Based on Path-Metrics for Modern ReLU Network Parameterizations
|
https://openreview.net/forum?id=T8VLY1KuOz
|
[
"Antoine Gonon",
"Nicolas Brisebarre",
"Elisa Riccietti",
"Rémi Gribonval"
] |
Poster
|
Robustness with respect to weight perturbations underpins guarantees for generalization, pruning and quantization. Existing
guarantees rely on *Lipschitz bounds in parameter space*, cover only plain feed-forward MLPs, and break under the ubiquitous neuron-wise rescaling symmetry of ReLU networks. We prove a new Lipschitz inequality expressed through the $\ell^{1}$-*path-metric* of the weights. The bound is (i) *rescaling-invariant* by construction and (ii) applies to any ReLU-DAG architecture with any combination of
convolutions, skip connections, pooling, and frozen (inference-time) batch-normalization —thus encompassing ResNets, U-Nets, VGG-style CNNs, and more. By respecting the network’s natural symmetries, the new bound strictly sharpens prior parameter-space bounds and can be computed in two forward passes. To illustrate its utility, we derive from it a symmetry-aware pruning criterion and
show—through a proof-of-concept experiment on a ResNet-18 trained on ImageNet—that its pruning performance matches that of classical magnitude pruning, while becoming totally immune to arbitrary neuron-wise rescalings.
|
relu neural networks, Lipschitz, rescaling-symmetry, path-lifting
|
This paper bounds function distances between ReLU networks via a metric that is invariant to rescaling-symmetries.
| 798
|
2405.15006
|
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] | null |
2,926
|
HybridGS: High-Efficiency Gaussian Splatting Data Compression using Dual-Channel Sparse Representation and Point Cloud Encoder
|
https://openreview.net/forum?id=6mQv4fnsj0
|
[
"Qi Yang",
"Le Yang",
"Geert Van der Auwera",
"Zhu Li"
] |
Poster
|
Most existing 3D Gaussian Splatting (3DGS) compression schemes focus on producing compact 3DGS representation via implicit data embedding. They have long encoding and decoding times and highly customized data format, making it difficult for widespread deployment. This paper presents a new 3DGS compression framework called HybridGS, which takes advantage of both compact generation and standardized point cloud data encoding. HybridGS first generates compact and explicit 3DGS data. A dual-channel sparse representation is introduced to supervise the primitive position and feature bit depth. It then utilizes a canonical point cloud encoder to carry out further data compression and form standard output bitstreams. A simple and effective rate control scheme is proposed to pivot the interpretable data compression scheme. HybridGS does not include any modules aimed at improving 3DGS quality during generation. But experiment results show that it still provides comparable reconstruction performance against state-of-the-art methods, with evidently faster encoding and decoding speed. The code is publicly available at https://github.com/Qi-Yangsjtu/HybridGS .
|
3D Gaussian Splatting, Compression, Sparse Representation
| null | 786
|
2505.01938
|
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] |
https://github.com/Qi-Yangsjtu/HybridGS
|
2,927
|
TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning
|
https://openreview.net/forum?id=bUGdGaNFhi
|
[
"Ron Shapira Weber",
"Shahar benishay",
"Andrey Lavrinenko",
"Shahaf E. Finder",
"Oren Freifeld"
] |
Poster
|
Fast and scalable alignment of time series is a fundamental challenge in many domains. The standard solution, Dynamic Time Warping (DTW), struggles with poor scalability and sensitivity to noise. We introduce TimePoint, a self-supervised method that dramatically accelerates DTW-based alignment while typically improving alignment accuracy by learning keypoints and descriptors from synthetic data. Inspired by 2D keypoint detection but carefully adapted to the unique challenges of 1D signals, TimePoint leverages efficient 1D diffeomorphisms, which effectively model nonlinear time warping, to generate realistic training data. This adaptation, along with fully convolutional and wavelet convolutional architectures, enables the extraction of informative keypoints and descriptors. Applying DTW to these sparse representations yields major speedups and typically higher alignment accuracy than standard DTW applied to the full signals. Despite being trained solely on synthetic data, TimePoint generalizes well to real-world time series. Extensive experiments demonstrate that TimePoint consistently achieves faster and more accurate alignments than standard DTW, making it a scalable solution for time-series analysis. Our code is available at https://github.com/
BGU-CS-VIL/TimePoint.
|
Time series, alignment, dtw, dynamic time warping
|
We present TimePoint, a self-supervised keypoints detection and descriptor Learning of time series
| 778
|
2505.23475
|
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] |
https://github.com/BGU-CS-VIL/TimePoint
|
2,928
|
KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems
|
https://openreview.net/forum?id=AKvy9a4jho
|
[
"Jusheng Zhang",
"Zimeng Huang",
"Yijia Fan",
"Ningyuan Liu",
"Mingyan Li",
"Zhuojie Yang",
"Jiawei Yao",
"Jian Wang",
"Keze Wang"
] |
Poster
|
As scaling large language models faces prohibitive costs, multi-agent systems emerge as a promising alternative, though challenged by static knowledge assumptions and coordination inefficiencies. We introduce Knowledge-Aware Bayesian Bandits (KABB), a novel framework that enhances multi-agent system coordination through semantic understanding and dynamic adaptation. The framework features three key innovations: a customized knowledge distance model for deep semantic understanding, a dual-adaptation mechanism for continuous expert optimization, and a knowledge-aware Thompson Sampling strategy for efficient expert selection. Extensive evaluation demonstrates KABB achieves an optimal cost-performance balance, maintaining high performance while keeping computational demands relatively low in multi-agent coordination.
|
LLM, Agent
|
LLM,Agent
| 774
|
2502.07350
|
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] | null |
2,929
|
Perception in Reflection
|
https://openreview.net/forum?id=YqerkYZfvu
|
[
"Yana Wei",
"Liang Zhao",
"Kangheng Lin",
"En Yu",
"Yuang Peng",
"Runpei Dong",
"Jianjian Sun",
"Haoran Wei",
"Zheng Ge",
"Xiangyu Zhang",
"Vishal M. Patel"
] |
Poster
|
We present a perception in reflection paradigm designed to transcend the limitations of current large vision-language models (LVLMs), which are expected yet often fail to achieve perfect perception initially. Specifically, we propose Reflective Perception (RePer), a dual-model reflection mechanism that systematically alternates between policy and critic models, enables iterative refinement of visual perception. This framework is powered by Reflective Perceptual Learning (RPL), which reinforces intrinsic reflective capabilities through a methodically constructed visual reflection dataset and reflective unlikelihood training Comprehensive experimental evaluation demonstrates RePer's quantifiable improvements in image understanding, captioning precision, and hallucination reduction. Notably, RePer achieves strong alignment between model attention patterns and human visual focus, while RPL optimizes fine-grained and free-form preference alignment. These advancements establish perception in reflection as a robust paradigm for future multimodal agents, particularly in tasks requiring complex reasoning and multi-step manipulation. Project Page: [https://weiyana.github.io/Perception-in-Reflection](https://weiyana.github.io/Perception-in-Reflection)
|
Perception, Reflection, LVLMs
| null | 766
|
2504.07165
|
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] |
https://github.com/weiyana/Perception-in-Reflection-ICML2025
|
2,930
|
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach
|
https://openreview.net/forum?id=uDTyhcOd9l
|
[
"Minting Pan",
"Yitao Zheng",
"Jiajian Li",
"Yunbo Wang",
"Xiaokang Yang"
] |
Poster
|
Offline reinforcement learning (RL) enables policy optimization using static datasets, avoiding the risks and costs of extensive real-world exploration. However, it struggles with suboptimal offline behaviors and inaccurate value estimation due to the lack of environmental interaction. We present Video-Enhanced Offline RL (VeoRL), a model-based method that constructs an interactive world model from diverse, unlabeled video data readily available online. Leveraging model-based behavior guidance, our approach transfers commonsense knowledge of control policy and physical dynamics from natural videos to the RL agent within the target domain. VeoRL achieves substantial performance gains (over 100% in some cases) across visual control tasks in robotic manipulation, autonomous driving, and open-world video games. Project page: https://panmt.github.io/VeoRL.github.io.
|
Offline Reinforcement Learning, Model-based Reinforcement Learning, Visuomotor Control
| null | 765
|
2505.06482
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] | null |
2,931
|
Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning
|
https://openreview.net/forum?id=73EwiOrN8W
|
[
"Seungho Baek",
"taegeon park",
"Jongchan Park",
"Seungjun Oh",
"Yusung Kim"
] |
Poster
|
Existing offline hierarchical reinforcement learning methods rely on high-level policy learning to generate subgoal sequences. However, their efficiency degrades as task horizons increase, and they lack effective strategies for stitching useful state transitions across different trajectories. We propose Graph-Assisted Stitching (GAS), a novel framework that formulates subgoal selection as a graph search problem rather than learning an explicit high-level policy. By embedding states into a Temporal Distance Representation (TDR) space, GAS clusters semantically similar states from different trajectories into unified graph nodes, enabling efficient transition stitching. A shortest-path algorithm is then applied to select subgoal sequences within the graph, while a low-level policy learns to reach the subgoals. To improve graph quality, we introduce the Temporal Efficiency (TE) metric, which filters out noisy or inefficient transition states, significantly enhancing task performance. GAS outperforms prior offline HRL methods across locomotion, navigation, and manipulation tasks. Notably, in the most stitching-critical task, it achieves a score of 88.3, dramatically surpassing the previous state-of-the-art score of 1.0. Our source code is available at: https://github.com/qortmdgh4141/GAS.
|
Offline Hierarchical Reinforcement Learning, Offline Goal-Conditioned Reinforcement Learning, Graph-based Reinforcement Learning, Temporal Distance Representation Learning
|
We propose a graph-based offline HRL framework that significantly improves long-horizon reasoning and trajectory stitching.
| 758
|
2506.07744
|
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] |
https://github.com/qortmdgh4141/GAS
|
2,932
|
Physics Aware Neural Networks for Unsupervised Binding Energy Prediction
|
https://openreview.net/forum?id=ZPqj44Knpd
|
[
"Ke Liu",
"Hao Chen",
"Chunhua Shen"
] |
Poster
|
Developing models for protein-ligand interactions holds substantial significance for drug discovery. Supervised methods often failed due to the lack of labeled data for predicting the protein-ligand binding energy, like antibodies. Therefore, unsupervised approaches are urged to make full use of the unlabeled data. To tackle the problem, we propose an efficient, unsupervised protein-ligand binding energy prediction model via the conservation of energy (CEBind), which follows the physical laws. Specifically, given a protein-ligand complex, we randomly sample forces for each atom in the ligand. Then these forces are applied rigidly to the ligand to perturb its position, following the law of rigid body dynamics. Finally, CEBind predicts the energy of both the unperturbed complex and the perturbed complex. The energy gap between two complexes equals the work of the outer forces, following the law of conservation of energy. Extensive experiments are conducted on the unsupervised protein-ligand binding energy prediction benchmarks, comparing them with previous works. Empirical results and theoretic analysis demonstrate that CEBind is more efficient and outperforms previous unsupervised models on benchmarks.
|
Protein-ligand binding
| null | 757
| null |
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] | null |
2,933
|
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning
|
https://openreview.net/forum?id=yIfCq03hsM
|
[
"Ian Gemp",
"Andreas Alexander Haupt",
"Luke Marris",
"Siqi Liu",
"Georgios Piliouras"
] |
Poster
|
Behavioral diversity, expert imitation, fairness, safety goals and others give rise to preferences in sequential decision making domains that do not decompose additively across time. We introduce the class of convex Markov games that allow general convex preferences over occupancy measures. Despite infinite time horizon and strictly higher generality than Markov games, pure strategy Nash equilibria exist. Furthermore, equilibria can be approximated empirically by performing gradient descent on an upper bound of exploitability. Our experiments reveal novel solutions to classic repeated normal-form games, find fair solutions in a repeated asymmetric coordination game, and prioritize safe long-term behavior in a robot warehouse environment. In the prisoner's dilemma, our algorithm leverages transient imitation to find a policy profile that deviates from observed human play only slightly, yet achieves higher per-player utility while also being three orders of magnitude less exploitable.
|
Markov Decision Process, Nash Equilibrium, Markov Game, N-player General-Sum, Convex Optimization, Occupancy Measure
|
convex MDPs + multi-agent = convex Markov Game. Non-trivial Nash existence proof. Powerful notions of imitation, exploration, and more enabled by occupancy-measure view.
| 751
|
2410.16600
|
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] | null |
2,934
|
Offline Opponent Modeling with Truncated Q-driven Instant Policy Refinement
|
https://openreview.net/forum?id=LNfiFWccxn
|
[
"Yuheng Jing",
"Kai Li",
"Bingyun Liu",
"Ziwen Zhang",
"Haobo Fu",
"QIANG FU",
"Junliang Xing",
"Jian Cheng"
] |
Poster
|
Offline Opponent Modeling (OOM) aims to learn an adaptive autonomous agent policy that dynamically adapts to opponents using an offline dataset from multi-agent games. Previous work assumes that the dataset is optimal. However, this assumption is difficult to satisfy in the real world. When the dataset is suboptimal, existing approaches struggle to work. To tackle this issue, we propose a simple and general algorithmic improvement framework, Truncated Q-driven Instant Policy Refinement (TIPR), to handle the suboptimality of OOM algorithms induced by datasets. The TIPR framework is plug-and-play in nature. Compared to original OOM algorithms, it requires only two extra steps: (1) Learn a horizon-truncated in-context action-value function, namely Truncated Q, using the offline dataset. The Truncated Q estimates the expected return within a fixed, truncated horizon and is conditioned on opponent information. (2) Use the learned Truncated Q to instantly decide whether to perform policy refinement and to generate policy after refinement during testing. Theoretically, we analyze the rationale of Truncated Q from the perspective of No Maximization Bias probability. Empirically, we conduct extensive comparison and ablation experiments in four representative competitive environments. TIPR effectively improves various OOM algorithms pretrained with suboptimal datasets.
|
Opponent Modeling, Offline, In-context Learning
|
We propose TIPR, a plug-and-play framework that instantly refines policies during test time using an in-context horizon-truncated Q function, effectively improving offline opponent modeling algorithms trained on suboptimal datasets.
| 747
| null |
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] | null |
2,935
|
A Chaotic Dynamics Framework Inspired by Dorsal Stream for Event Signal Processing
|
https://openreview.net/forum?id=vCtyqomphj
|
[
"Yu Chen",
"Jing Lian",
"Zhaofei Yu",
"Jizhao Liu",
"Jisheng Dang",
"Gang Wang"
] |
Poster
|
Event cameras are bio-inspired vision sensors that encode visual information with high dynamic range, high temporal resolution, and low latency. Current state-of-the-art event stream processing methods rely on end-to-end deep learning techniques. However, these models are heavily dependent on data structures, limiting their stability and generalization capabilities across tasks, thereby hindering their deployment in real-world scenarios. To address this issue, we propose a chaotic dynamics event signal processing framework inspired by the dorsal visual pathway of the brain. Specifically, we utilize Continuous-coupled Neural Network (CCNN) to encode the event stream. CCNN encodes polarity-invariant event sequences as periodic signals and polarity-changing event sequences as chaotic signals. We then use continuous wavelet transforms to analyze the dynamical states of CCNN neurons and establish the high-order mappings of the event stream. The effectiveness of our method is validated through integration with conventional classification networks, achieving state-of-the-art classification accuracy on the N-Caltech101 and N-CARS datasets, with results of 84.3% and 99.9%, respectively. Our method improves the accuracy of event camera-based object classification while significantly enhancing the generalization and stability of event representation.
|
Event Camera, Object Classification, Continuous-coupled Neural Network.
| null | 742
| null |
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] | null |
2,936
|
FairPFN: A Tabular Foundation Model for Causal Fairness
|
https://openreview.net/forum?id=I8DVh2jnEA
|
[
"Jake Robertson",
"Noah Hollmann",
"Samuel Müller",
"Noor Awad",
"Frank Hutter"
] |
Poster
|
Machine learning (ML) systems are utilized in critical sectors such as healthcare, law enforcement, and finance, but often rely on historical data that contains demographic biases, leading to decisions that perpetuate or intensify existing inequalities. Causal and counterfactual fairness provide a transparent, human-in-the-loop framework to mitigate algorithmic discrimination, aligning closely with legal doctrines of direct and indirect discrimination. However, current causal fairness frameworks hold a key limitation in that they assume prior knowledge of the correct causal model, restricting their applicability in complex fairness scenarios where causal models are unknown or difficult to identify. To bridge this gap, we propose FairPFN, a tabular foundation model pre-trained on synthetic causal fairness data to identify and mitigate the causal effects of protected attributes in its predictions. FairPFN's key contribution is that it requires no knowledge of the causal model and demonstrates strong performance across a diverse set of hand-crafted and real-world causal scenarios relative to robust baseline methods. FairPFN paves the way for a promising direction for future research, making causal fairness more accessible to a wider variety of complex fairness problems.
|
Causal Fairness, Foundation Models, In-Context-Learning, Prior-Fitted-Networks
| null | 738
|
2506.07049
|
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] |
https://github.com/jr2021/FairPFN
|
2,937
|
Pfeife: Automatic Pipeline Parallelism for PyTorch
|
https://openreview.net/forum?id=FSq95c0wb3
|
[
"Ho Young Jhoo",
"Chung-Kil Hur",
"Nuno P. Lopes"
] |
Poster
|
The memory requirements of machine learning (ML) models has been growing quickly. However, the memory capacity of GPUs has not kept pace. Despite significant research on reducing the memory usage of ML models, the larger models do not fit in a single device. A popular solution to the memory capacity issue is to use multiple devices in parallel. In this paper, we focus on a particular form of parallelism called pipelining, as it offers a good balance between cost and performance for many ML models. We present Pfeife, the first tool that integrates with PyTorch to provide automatic pipelining of ML models. Pfeife intercepts the execution of models and parallelizes them transparently, requiring no manual work. We show that Pfeife can execute large models that would otherwise not run due to not fitting in a single device. Moreover, Pfeife can pipeline non-sequential models such as Stable Diffusion, which are not supported by existing pipelining parallelism tools. Pfeife outperforms state-of-the-art tools by up to 22%.
|
Parallel Training, Automatic Parallelism, Pipeline Parallelism, PyTorch
|
Implementation of fast and general automatic pipeline parallelism for PyTorch using torch.compile.
| 726
| null |
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] |
https://github.com/MerHS/pfeife
|
2,938
|
Inductive Gradient Adjustment for Spectral Bias in Implicit Neural Representations
|
https://openreview.net/forum?id=pYMZQtkp3F
|
[
"Kexuan Shi",
"Hai Chen",
"Leheng Zhang",
"Shuhang Gu"
] |
Poster
|
Implicit Neural Representations (INRs), as a versatile representation paradigm, have achieved success in various computer vision tasks. Due to the spectral bias of the vanilla multi-layer perceptrons (MLPs), existing methods focus on designing MLPs with sophisticated architectures or repurposing
training techniques for highly accurate INRs. In this paper, we delve into the linear dynamics model of MLPs and theoretically identify the empirical Neural Tangent Kernel (eNTK) matrix as a reliable link between spectral bias and training dynamics. Based on this insight, we propose a practical **I**nductive **G**radient **A**djustment (**IGA**) method, which could purposefully improve the spectral bias via inductive generalization of eNTK-based gradient transformation matrix. Theoretical and
empirical analyses validate impacts of IGA on spectral bias. Further, we evaluate our method on different INRs tasks with various INR architectures and compare to existing training techniques. The superior and consistent improvements clearly validate the advantage of our IGA. Armed with our gradient adjustment method, better INRs with more enhanced texture details and sharpened edges can be learned from data by tailored impacts on spectral bias. The codes are available at: [https://github.com/LabShuHangGU/IGA-INR](https://github.com/LabShuHangGU/IGA-INR).
|
Spectral Bias, Implicit Neural Representations, Training Dynamics, Inductive Gradient Adjustment
|
Spectral Bias, Implicit Neural Representations, Training Dynamics
| 724
|
2410.13271
|
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] |
https://github.com/LabShuHangGU/IGA-INR
|
2,939
|
SAND: One-Shot Feature Selection with Additive Noise Distortion
|
https://openreview.net/forum?id=m7kvl1es0S
|
[
"Pedram Pad",
"Hadi Hammoud",
"Mohamad Dia",
"Nadim Maamari",
"Liza Andrea Dunbar"
] |
Poster
|
Feature selection is a critical step in data-driven applications, reducing input dimensionality to enhance learning accuracy, computational efficiency, and interpretability. Existing state-of-the-art methods often require post-selection retraining and extensive hyperparameter tuning, complicating their adoption. We introduce a novel, non-intrusive feature selection layer that, given a target feature count $k$, automatically identifies and selects the $k$ most informative features during neural network training. Our method is uniquely simple, requiring no alterations to the loss function, network architecture, or post-selection retraining. The layer is mathematically elegant and can be fully described by:
\begin{align}
\nonumber
\tilde{x}_i = a_i x_i + (1-a_i)z_i
\end{align}
where $x_i$ is the input feature, $\tilde{x}_i$ the output, $z_i$ a Gaussian noise, and $a_i$ trainable gain such that $\sum_i{a_i^2}=k$.
This formulation induces an automatic clustering effect, driving $k$ of the $a_i$ gains to $1$ (selecting informative features) and the rest to $0$ (discarding redundant ones) via weighted noise distortion and gain normalization. Despite its extreme simplicity, our method achieves competitive performance on standard benchmark datasets and a novel real-world dataset, often matching or exceeding existing approaches without requiring hyperparameter search for $k$ or retraining. Theoretical analysis in the context of linear regression further validates its efficacy. Our work demonstrates that simplicity and performance are not mutually exclusive, offering a powerful yet straightforward tool for feature selection in machine learning.
|
feature selection, machine learning, neural networks
|
Novel and efficient end-to-end feature selection for NN. Simple implementation, no alteration of loss function or model architecture, full control over the number of features without tuning or search, outperforms SOTA, theoretical insights provided.
| 723
|
2505.03923
|
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] |
https://github.com/csem/SAND
|
2,940
|
Antidote: Post-fine-tuning Safety Alignment for Large Language Models against Harmful Fine-tuning Attack
|
https://openreview.net/forum?id=Arepl4R86m
|
[
"Tiansheng Huang",
"Gautam Bhattacharya",
"Pratik Joshi",
"Joshua Kimball",
"Ling Liu"
] |
Poster
|
Safety aligned Large Language Models (LLMs) are vulnerable to harmful fine-tuning attacks -- a few harmful data mixed in the fine-tuning dataset can break the LLMs's safety alignment. While several defenses have been proposed, our evaluation shows that existing defenses fail \textit{when some specific training hyper-parameters are chosen} -- a large learning rate or a large number of training epochs in the fine-tuning stage can easily invalidate the defense. To this end, we propose Antidote, a post-fine-tuning stage solution, which remains \textbf{\textit{agnostic to the training hyper-parameters in the fine-tuning stage}}. Antidote relies on the philosophy that by removing the harmful parameters, the harmful model can be recovered from the harmful behaviors, regardless of how those harmful parameters are formed in the fine-tuning stage. With this philosophy, we introduce a one-shot pruning stage after harmful fine-tuning to remove the harmful weights that are responsible for the generation of harmful content. Despite its embarrassing simplicity, empirical results show that Antidote can reduce harmful score while maintaining accuracy on downstream tasks.
|
Harmful fine-tuning attack, Large language models
|
This paper proposes a post-fine-tuning stage defense for harmful fine-tuning attack.
| 721
| null |
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] | null |
2,941
|
Guided Structural Inference: Leveraging Priors with Soft Gating Mechanisms
|
https://openreview.net/forum?id=g6XtPA0DQp
|
[
"Aoran Wang",
"Xinnan Dai",
"Jun Pang"
] |
Poster
|
Existing methods for inferring latent relational structures struggle to integrate partial prior knowledge, such as known edges, node-degree constraints, and global sparsity, without destabilizing training or conflicting with probabilistic assumptions. We propose Soft-Gated Structural Inference (SGSI), a VAE framework that seamlessly incorporates domain constraints via (1) soft gating with learnable edge masks to preserve gradients, (2) cloning-clamping of deterministic edges to avoid distributional conflicts, and (3) adaptive regularization to balance data-driven learning with domain constraints. By excluding known edges from stochastic inference, SGSI reallocates capacity to uncertain interactions, optimizing the information bottleneck trade-off. Experiments on 16 datasets show SGSI improves edge recovery by up to $9$\% AUROC over baselines, scales to larger graphs ($94.2$\% AUROC), and maintains stable training. SGSI bridges domain expertise with data-driven learning, enabling interpretable and robust structural discovery in dynamical systems.
|
Structural Inference, Prior Knowledge, Dynamical Systems
| null | 699
| null |
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] |
https://github.com/wang422003/SGSI-Guided-Structural-Inference-Leveraging-Priors-with-Soft-Gating-Mechanisms
|
2,942
|
Core Context Aware Transformers for Long Context Language Modeling
|
https://openreview.net/forum?id=MAHPZNduS4
|
[
"Yaofo Chen",
"Zeng You",
"Shuhai Zhang",
"Haokun Li",
"Yirui Li",
"Yaowei Wang",
"Mingkui Tan"
] |
Poster
|
Transformer-based Large Language Models (LLMs) have exhibited remarkable success in extensive tasks primarily attributed to self-attention mechanism, which requires a token to consider all preceding tokens as its context to compute attention. However, when the context length L becomes very large (e.g., 128K), the amount of potentially redundant information in the context tends to increase. The redundant context not only hampers the modeling representation performance but also incurs unnecessary computational and storage overhead. In this paper, we propose a plug-and-play Core Context Aware (CCA) Attention for efficient long-context modeling, comprising two complementary modules: 1) Globality-aware pooling module groups input tokens and dynamically compresses each group into one core token based on their significance. In this way, our method automatically focuses and strengthens core context while diminishing redundancy during the learning process, leading to effective long-term dependency modeling. 2) Locality-preserving module incorporates neighboring tokens to preserve local context for detailed representation. Notably, our CCA-Attention is able to replace the self-attention module in existing LLMs with minimal fine-tuning cost. Extensive experimental results show the superiority of our method in both long-context modeling and computational efficiency over state-of-the-art methods.
|
Large Language Models; Efficient Attention
|
We propose Core Context Aware Attention that greatly reduces the context redundancy with strong performance on long context modeling.
| 692
|
2412.12465
|
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] |
https://github.com/chenyaofo/CCA-Attention
|
2,943
|
LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
|
https://openreview.net/forum?id=UWTz4ai3FZ
|
[
"Hang Gao",
"Huang Wenxuan",
"Fengge Wu",
"Zhao Junsuo",
"Changwen Zheng",
"Huaping Liu"
] |
Poster
|
The use of large language models (LLMs) as feature enhancers to optimize node representations, which are then used as inputs for graph neural networks (GNNs), has shown significant potential in graph representation learning. However, the fundamental properties of this approach remain underexplored. To address this issue, we propose conducting a more in-depth analysis of this issue based on the interchange intervention method. First, we construct a synthetic graph dataset with controllable causal relationships, enabling precise manipulation of semantic relationships and causal modeling to provide data for analysis. Using this dataset, we conduct interchange interventions to examine the deeper properties of LLM enhancers and GNNs, uncovering their underlying logic and internal mechanisms. Building on the analytical results, we design a plug-and-play optimization module to improve the information transfer between LLM enhancers and GNNs. Experiments across multiple datasets and models validate the proposed module.
|
Large language models; Graph Neural Netwroks; Causal
|
This study analyzes the under-explored properties of LLM-enhanced GNNs using interchange interventions and proposes an optimization module to improve information transfer.
| 682
|
2505.08265
|
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] |
https://github.com/WX4code/LLMEnhCausalMechanism
|
2,944
|
Visual Abstraction: A Plug-and-Play Approach for Text-Visual Retrieval
|
https://openreview.net/forum?id=DDvInyGVuz
|
[
"GuofengDing",
"Yiding Lu",
"Peng Hu",
"Mouxing Yang",
"Yijie Lin",
"Xi Peng"
] |
Poster
|
Text-to-visual retrieval often struggles with semantic redundancy and granularity mismatches between textual queries and visual content. Unlike existing methods that address these challenges during training, we propose VISual Abstraction (VISA), a test-time approach that enhances retrieval by transforming visual content into textual descriptions using off-the-shelf large models. The generated text descriptions, with their dense semantics, naturally filter out low-level redundant visual information. To further address granularity issues, VISA incorporates a question-answering process, enhancing the text description with the specific granularity information requested by the user. Extensive experiments demonstrate that VISA brings substantial improvements in text-to-image and text-to-video retrieval for both short- and long-context queries, offering a plug-and-play enhancement to existing retrieval systems.
|
text-to-image retrieval, text-to-video retrieval, plug-and-play
| null | 681
| null |
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] | null |
2,945
|
Towards Trustworthy Federated Learning with Untrusted Participants
|
https://openreview.net/forum?id=PjadKnUson
|
[
"Youssef Allouah",
"Rachid Guerraoui",
"John Stephan"
] |
Poster
|
Resilience against malicious participants and data privacy are essential for trustworthy federated learning, yet achieving both with good utility typically requires the strong assumption of a trusted central server. This paper shows that a significantly weaker assumption suffices: each pair of participants shares a randomness seed unknown to others.
In a setting where malicious participants may collude with an untrusted server, we propose CafCor, an algorithm that integrates robust gradient aggregation with correlated noise injection, using shared randomness between participants.
We prove that CafCor achieves strong privacy-utility trade-offs, significantly outperforming local differential privacy (DP) methods, which do not make any trust assumption, while approaching central DP utility, where the server is fully trusted.
Empirical results on standard benchmarks validate CafCor's practicality, showing that privacy and robustness can coexist in distributed systems without sacrificing utility or trusting the server.
|
distributed machine learning, differential privacy, robustness, federated learning, trustworthy machine learning
| null | 677
|
2505.01874
|
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] | null |
2,946
|
Contrastive Visual Data Augmentation
|
https://openreview.net/forum?id=y9JV6VANYp
|
[
"Yu Zhou",
"Bingxuan Li",
"Tang Mohan",
"Xiaomeng Jin",
"Te-Lin Wu",
"Kuan-Hao Huang",
"Heng Ji",
"Kai-Wei Chang",
"Nanyun Peng"
] |
Poster
|
Large multimodal models (LMMs) often struggle to recognize novel concepts, as they rely on pre-trained knowledge and have limited ability to capture subtle visual details. Domain-specific knowledge gaps in training also make them prone to confusing visually similar, commonly misrepresented, or low-resource concepts. To help LMMs better align nuanced visual features with language, improving their ability to recognize and reason about novel or rare concepts, we propose a Contrastive visual Data Augmentation (CoDA) strategy. CoDA extracts key contrastive textual and visual features of target concepts against the known concepts they are misrecognized as, and then uses multimodal generative models to produce targeted synthetic data. Automatic filtering of extracted features and augmented images is implemented to guarantee their quality, as verified by human annotators. We show the effectiveness and efficiency of CoDA on low-resource concept and diverse scene recognition datasets including INaturalist and SUN. We additionally collect NovelSpecies, a benchmark dataset consisting of newly discovered animal species that are guaranteed to be unseen by LMMs. LLaVA-1.6 1-shot updating results on these three datasets show CoDA significantly improves SOTA visual data augmentation strategies by 12.3% (NovelSpecies), 5.1% (SUN), and 6.0% (iNat) absolute gains in accuracy.
|
Data Augmentation, Text-to-Image Generation, Feature Extraction
|
We introduce the Contrastive Visual Data Augmentation strategy and the NovelSpecies dataset.
| 672
|
2502.17709
|
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] | null |
2,947
|
MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance
|
https://openreview.net/forum?id=0epuNvt5Dj
|
[
"Zhixuan Chen",
"Xing Hu",
"Dawei Yang",
"Zukang Xu",
"XUCHEN",
"Zhihang Yuan",
"Sifan Zhou",
"JiangyongYu"
] |
Poster
|
Mixture-of-Experts (MoE) large language models (LLMs), which leverage dynamic routing and sparse activation to enhance efficiency and scalability, have achieved higher performance while reducing computational costs. However, these models face significant memory overheads, limiting their practical deployment and broader adoption. Post-training quantization (PTQ), a widely used method for compressing LLMs, encounters severe accuracy degradation and diminished generalization performance when applied to MoE models. This paper investigates the impact of MoE’s sparse and dynamic characteristics on quantization and identifies two primary challenges: (1) Inter-expert imbalance, referring to the uneven distribution of samples across experts, which leads to insufficient and biased calibration for less frequently utilized experts; (2) Intra-expert imbalance, arising from MoE's unique aggregation mechanism, which leads to varying degrees of correlation between different samples and their assigned experts. To address these challenges, we propose MoEQuant, a novel quantization framework tailored for MoE LLMs. MoEQuant includes two novel techniques: 1) Expert-Balanced Self-Sampling (EBSS) is an efficient sampling method that efficiently constructs a calibration set with balanced expert distributions by leveraging the cumulative probabilities of tokens and expert balance metrics as guiding factors. 2) Affinity-Guided Quantization (AGQ), which incorporates affinities between experts and samples into the quantization process, thereby accurately assessing the impact of individual samples on different experts within the MoE layer. Experiments demonstrate that MoEQuant achieves substantial performance gains (more than 10 points accuracy gain in the HumanEval for DeepSeekMoE-16B under 4-bit quantization) and boosts efficiency.
|
Mixture of Expert, Large Language Models, Quantization
| null | 658
|
2505.03804
|
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] |
https://github.com/chenzx921020/MoEQuant
|
2,948
|
Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation
|
https://openreview.net/forum?id=dUq579lefp
|
[
"Bowen Zheng",
"Tianming Yang"
] |
Poster
|
Diffusion distillation is a widely used technique to reduce the sampling cost of diffusion models, yet it often requires extensive training, and the student performance tends to be degraded. Recent studies show that incorporating a GAN objective may alleviate these issues, yet the underlying mechanism remains unclear. In this work, we first identify a key limitation of distillation: mismatched step sizes and parameter numbers between the teacher and the student model lead them to converge to different local minima, rendering direct imitation suboptimal. We further demonstrate that a standalone GAN objective, without relying a distillation loss, overcomes this limitation and is sufficient to convert diffusion models into efficient one-step generators. Based on this finding, we propose that diffusion training may be viewed as a form of generative pre-training, equipping models with capabilities that can be unlocked through lightweight GAN fine-tuning. Supporting this view, we create a one-step generation model by fine-tuning a pre-trained model with 85% of parameters frozen, achieving strong performance with only 0.2M images and near-SOTA results with 5M images. We further present a frequency-domain analysis that may explain the one-step generative capability gained in diffusion training. Overall, our work provides a new perspective for diffusion training, highlighting its role as a powerful generative pre-training process, which can be the basis for building efficient one-step generation models.
|
Diffusion Models, One-Step Generators, Generative Models, Image Generation, GAN
| null | 648
|
2506.09376
|
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] | null |
2,949
|
Unlocking the Power of SAM 2 for Few-Shot Segmentation
|
https://openreview.net/forum?id=TQtUTC3eKv
|
[
"Qianxiong Xu",
"Lanyun Zhu",
"Xuanyi Liu",
"Guosheng Lin",
"Cheng Long",
"Ziyue Li",
"Rui Zhao"
] |
Poster
|
Few-Shot Segmentation (FSS) aims to learn class-agnostic segmentation on few classes to segment arbitrary classes, but at the risk of overfitting. To address this, some methods use the well-learned knowledge of foundation models (e.g., SAM) to simplify the learning process. Recently, SAM 2 has extended SAM by supporting video segmentation, whose class-agnostic matching ability is useful to FSS. A simple idea is to encode support foreground (FG) features as memory, with which query FG features are matched and fused. Unfortunately, the FG objects in different frames of SAM 2's video data are always the same identity, while those in FSS are different identities, i.e., the matching step is incompatible. Therefore, we design Pseudo Prompt Generator to encode pseudo query memory, matching with query features in a compatible way. However, the memories can never be as accurate as the real ones, i.e., they are likely to contain incomplete query FG, and some unexpected query background (BG) features, leading to wrong segmentation. Hence, we further design Iterative Memory Refinement to fuse more query FG features into the memory, and devise a Support-Calibrated Memory Attention to suppress the unexpected query BG features in memory. Extensive experiments have been conducted on PASCAL-5$^i$ and COCO-20$^i$ to validate the effectiveness of our design, e.g., the 1-shot mIoU can be 4.2\% better than the best baseline.
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Few-Shot Segmentation, Segment Anything Model 2, Video, Intra-class Gap
| null | 633
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2505.14100
|
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] |
https://github.com/Sam1224/FSSAM
|
2,950
|
Approximation to Smooth Functions by Low-Rank Swish Networks
|
https://openreview.net/forum?id=ZWTb5UBdpG
|
[
"Zimeng Li",
"Hongjun LI",
"Jingyuan Wang",
"Ke Tang"
] |
Poster
|
While deep learning has witnessed remarkable achievements in a wide range of applications, its substantial computational cost imposes limitations on application scenarios of neural networks. To alleviate this problem, low-rank compression is proposed as a class of efficient and hardware-friendly network compression methods, which reduce computation by replacing large matrices in neural networks with products of two small ones. In this paper, we implement low-rank networks by inserting a sufficiently narrow linear layer without bias between each of two adjacent nonlinear layers. We prove that low-rank Swish networks with a fixed depth are capable of approximating any function from the Hölder ball $\mathcal{C}^{\beta, R}([0,1]^d)$ within an arbitrarily small error where $\beta$ is the smooth parameter and $R$ is the radius. Our proposed constructive approximation ensures that the width of linear hidden layers required for approximation is no more than one-third of the width of nonlinear layers, which implies that the computational cost can be decreased by at least one-third compared with a network with the same depth and width of nonlinear layers but without narrow linear hidden layers. Our theoretical finding can offer a theoretical basis for low-rank compression from the perspective of universal approximation theory.
|
Neural Network, Swish Activation Function, Universal Approximation Theory, Network Compression, Low-Rank Compression, Low-Rank Factorization
|
We offer a theoretical basis for low-rank compression from the perspective of universal approximation theory by proving any Hölder function can be approximated by a Swish network with low-rank weight matrices.
| 627
| null |
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] | null |
2,951
|
SpeCache: Speculative Key-Value Caching for Efficient Generation of LLMs
|
https://openreview.net/forum?id=PQIrsaIQdn
|
[
"Shibo Jie",
"Yehui Tang",
"Kai Han",
"Zhi-Hong Deng",
"Jing Han"
] |
Poster
|
Transformer-based large language models (LLMs) have already achieved remarkable results on long-text tasks, but the limited GPU memory (VRAM) resources struggle to accommodate the linearly growing demand for key-value (KV) cache as the sequence length increases, which has become a bottleneck for the application of LLMs on long sequences. Existing KV cache compression methods include eviction, merging, or quantization of the KV cache to reduce its size. However, compression results in irreversible information forgetting, potentially affecting the accuracy of subsequent decoding. In this paper, we propose SpeCache, which takes full advantage of the large and easily expandable CPU memory to offload the complete KV cache, and dynamically fetches KV pairs back in each decoding step based on their importance measured by low-precision KV cache copy in VRAM. To avoid inference latency caused by CPU-GPU communication, SpeCache speculatively predicts the KV pairs that the next token might attend to, allowing us to prefetch them before the next decoding step which enables parallelization of prefetching and computation. Experiments on LongBench and Needle-in-a-Haystack benchmarks verify that SpeCache effectively reduces VRAM usage while avoiding information forgetting for long sequences without re-training, even with a 10x high KV cache compression ratio.
|
Key-Value Cache, Large Language Models
| null | 625
|
2503.16163
|
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] | null |
2,952
|
Rethinking the Temperature for Federated Heterogeneous Distillation
|
https://openreview.net/forum?id=f9xsNQ8oSd
|
[
"Fan Qi",
"Daxu Shi",
"Chuokun Xu",
"Shuai Li",
"Changsheng Xu"
] |
Poster
|
Federated Distillation (FedKD) relies on lightweight knowledge carriers like logits for efficient client-server communication.
Although logit-based methods have demonstrated promise in addressing statistical and architectural heterogeneity in federated learning (FL), current approaches remain constrained by suboptimal temperature calibration during knowledge fusion.
To address these limitations, we propose ReT-FHD, a framework featuring: 1) Multi-level Elastic Temperature, which dynamically adjusts distillation intensities across model layers, achieving optimized knowledge transfer between heterogeneous local models; 2) Category-Aware Global Temperature Scaling that implements class-specific temperature calibration based on confidence distributions in global logits, enabling personalized distillation policies; 3) Z-Score Guard, a blockchain-verified validation mechanism mitigating 44\% of label-flipping and model poisoning attacks. Evaluations across diverse benchmarks with varying model/data heterogeneity demonstrate that the ReT-FHD achieves significant accuracy improvements over baseline methods while substantially reducing communication costs compared to existing approaches. Our work establishes that properly calibrated logits can serve as self-sufficient carriers for building scalable and secure heterogeneous FL systems.
|
Heterogeneous Federated Learning, Logits Distillation
|
Dynamic Temperature Design for Federated Heterogeneous Logits Distillation.
| 600
| null |
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] | null |
2,953
|
Can Transformers Learn Full Bayesian Inference in Context?
|
https://openreview.net/forum?id=9Ip6fihKbc
|
[
"Arik Reuter",
"Tim G. J. Rudner",
"Vincent Fortuin",
"David Rügamer"
] |
Poster
|
Transformers have emerged as the dominant architecture in the field of deep learning, with a broad range of applications and remarkable in-context learning (ICL) capabilities. While not yet fully understood, ICL has already proved to be an intriguing phenomenon, allowing transformers to learn in context—without requiring further training. In this paper, we further advance the understanding of ICL by demonstrating that transformers can perform full Bayesian inference for commonly used statistical models in context. More specifically, we introduce a general framework that builds on ideas from prior fitted networks and continuous normalizing flows and enables us to infer complex posterior distributions for models such as generalized linear models and latent factor models. Extensive experiments on real-world datasets demonstrate that our ICL approach yields posterior samples that are similar in quality to state-of-the-art MCMC or variational inference methods that do not operate in context. The source code for this paper is available at https://github.com/ArikReuter/ICL_for_Full_Bayesian_Inference
|
In-Context Learning, Prior-Data Fitted Networks, Bayesian Inference
|
We show that transfomers can effectively solve full Bayesian inference in context.
| 599
|
2501.16825
|
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] |
https://github.com/ArikReuter/ICL_for_Full_Bayesian_Inference
|
2,954
|
Learning Invariant Causal Mechanism from Vision-Language Models
|
https://openreview.net/forum?id=GB9XiKIwfp
|
[
"Zeen Song",
"Siyu Zhao",
"Xingyu Zhang",
"Jiangmeng Li",
"Changwen Zheng",
"Wenwen Qiang"
] |
Poster
|
Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success, but its performance can degrade when fine-tuned in out-of-distribution (OOD) scenarios. We model the prediction process using a Structural Causal Model (SCM) and show that the causal mechanism involving both invariant and variant factors in training environments differs from that in test environments. In contrast, the causal mechanism with solely invariant factors remains consistent across environments. We theoretically prove the existence of a linear mapping from CLIP embeddings to invariant factors, which can be estimated using interventional data. Additionally, we provide a condition to guarantee low OOD risk of the invariant predictor. Based on these insights, we propose the Invariant Causal Mechanism of CLIP (CLIP-ICM) framework. CLIP-ICM involves collecting interventional data, estimating a linear projection matrix, and making predictions within the invariant subspace. Experiments on several OOD datasets show that CLIP-ICM significantly improves the performance of CLIP. Our method offers a simple but powerful enhancement, boosting the reliability of CLIP in real-world applications.
|
Vision-Language Models, Causal representation Learning, Out-of-distribution generalization, Representation Learning
| null | 593
|
2405.15289
|
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] |
https://github.com/ZeenSong/CLIP-ICM
|
2,955
|
Lexico: Extreme KV Cache Compression via Sparse Coding over Universal Dictionaries
|
https://openreview.net/forum?id=Yh9vxlxnjA
|
[
"Junhyuck Kim",
"Jongho Park",
"Jaewoong Cho",
"Dimitris Papailiopoulos"
] |
Poster
|
We introduce Lexico, a novel KV cache compression method that leverages sparse coding with a universal dictionary. Our key finding is that key-value cache in modern LLMs can be accurately approximated using sparse linear combination from a small, input-agnostic dictionary of ~4k atoms, enabling efficient compression across different input prompts, tasks and models. Using orthogonal matching pursuit for sparse approximation, Lexico achieves flexible compression ratios through direct sparsity control. On GSM8K, across multiple model families (Mistral, Llama 3, Qwen2.5), Lexico maintains 90-95% of the original performance while using only 15-25% of the full KV-cache memory, outperforming both quantization and token eviction methods. Notably, Lexico remains effective in low memory regimes where 2-bit quantization fails, achieving up to 1.7x better compression on LongBench and GSM8K while maintaining high accuracy.
|
transformer, kv cache, compression
|
We introduce a novel KV cache compression method that leverages sparse coding with a universal dictionary.
| 592
|
2412.08890
|
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] |
https://github.com/krafton-ai/lexico
|
2,956
|
S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking
|
https://openreview.net/forum?id=vHr9cdeFfu
|
[
"Tao Tang",
"Lijun Zhou",
"Pengkun Hao",
"Zihang He",
"Kalok Ho",
"Shuo Gu",
"Zhihui Hao",
"Haiyang Sun",
"Kun Zhan",
"Peng Jia",
"XianPeng Lang",
"Xiaodan Liang"
] |
Poster
|
3D multiple object tracking (MOT) plays a crucial role in autonomous driving perception. Recent end-to-end query-based trackers simultaneously detect and track objects, which have shown promising potential for the 3D MOT task. However, existing methods are still in the early stages of development and lack systematic improvements, failing to track objects in certain complex scenarios, like occlusions and the small size of target object’s situations. In this paper, we first summarize the current end-to-end 3D MOT framework by decomposing it into three constituent parts: query initialization, query propagation, and query matching. Then we propose corresponding improvements, which lead to a strong yet simple tracker: S2-Track. Specifically, for query initialization, we present 2D-Prompted Query Initialization, which leverages predicted 2D object and depth information to prompt an initial estimate of the object’s 3D location. For query propagation, we introduce an Uncertainty-aware Probabilistic Decoder to capture the uncertainty of complex environment in object prediction with probabilistic attention. For query matching, we propose a Hierarchical Query Denoising strategy to enhance training robustness and convergence. As a result, our S2-Track achieves state-of-the-art performance on nuScenes benchmark, i.e., 66.3% AMOTA on test split, surpassing the previous best end-to-end solution by a significant margin of 8.9% AMOTA. We achieve 1st place on the nuScenes tracking task leaderboard.
|
3D Multi-Object Tracking, S2-Track
|
A simple yet strong approach for end-to-end 3D multi-object tracking
| 587
|
2406.02147
|
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] | null |
2,957
|
Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting
|
https://openreview.net/forum?id=b5h60xQnzM
|
[
"Siru Zhong",
"Weilin Ruan",
"Ming Jin",
"Huan Li",
"Qingsong Wen",
"Yuxuan Liang"
] |
Poster
|
Recent advancements in time series forecasting have explored augmenting models with text or vision modalities to improve accuracy. While text provides contextual understanding, it often lacks fine-grained temporal details. Conversely, vision captures intricate temporal patterns but lacks semantic context, limiting the complementary potential of these modalities. To address this, we propose Time-VLM, a novel multimodal framework that leverages pre-trained Vision-Language Models (VLMs) to bridge temporal, visual, and textual modalities for enhanced forecasting. Our framework comprises three key components: (1) a Retrieval-Augmented Learner, which extracts enriched temporal features through memory bank interactions; (2) a Vision-Augmented Learner, which encodes time series as informative images; and (3) a Text-Augmented Learner, which generates contextual textual descriptions. These components collaborate with frozen pre-trained VLMs to produce multimodal embeddings, which are then fused with temporal features for final prediction. Extensive experiments demonstrate that Time-VLM achieves superior performance, particularly in few-shot and zero-shot scenarios, thereby establishing a new direction for multimodal time series forecasting. Code is available at https://github.com/CityMind-Lab/ICML25-TimeVLM.
|
Time Series Forecasting, Vision-Language Models
|
A novel multimodal framework that leverages pre-trained Vision-Language Models (VLMs) to bridge temporal, visual, and textual modalities for augmented time series forecasting.
| 586
| null |
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] |
https://github.com/CityMind-Lab/ICML25-TimeVLM
|
2,958
|
Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms?
|
https://openreview.net/forum?id=q0JaH6Ukqb
|
[
"Yaolong Yu",
"Fan Yao",
"Sinno Jialin Pan"
] |
Poster
|
We employ a game-theoretic framework to study the impact of a specific strategic behavior among creators---group behavior---on recommendation platforms. In this setting, creators within a group collaborate to maximize their collective utility.
We show that group behavior has a limited effect on the game's equilibrium when the group size is small. However, when the group size is large, group behavior can significantly alter content distribution and user welfare.
Specifically, in a top-$K$ recommendation system with exposure-based rewards, we demonstrate that user welfare can suffer a significant loss due to group strategies, and user welfare does not necessarily increase with larger values of $K$ or more random matching, contrasting sharply with the individual creator case. Furthermore, we investigate user welfare guarantees through the lens of the Price of Anarchy (PoA). In the general case, we establish a negative result on the bound of PoA with exposure rewards, proving that it can be arbitrarily large. We then investigate a user engagement rewarding mechanism, which mitigates the issues caused by large group behavior, showing that $\text{PoA}\leq K+1$ in the general case and $\text{PoA}\leq 2$ in the binary case. Empirical results from simulations further support the effectiveness of the user engagement rewarding mechanism.
|
Recommendation system, content creator competition game, group strategy, price of anarchy
| null | 571
| null |
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] | null |
2,959
|
XAttnMark: Learning Robust Audio Watermarking with Cross-Attention
|
https://openreview.net/forum?id=zuOKPHHfEr
|
[
"Yixin Liu",
"Lie Lu",
"Jihui Jin",
"Lichao Sun",
"Andrea Fanelli"
] |
Poster
|
The rapid proliferation of generative audio synthesis and editing technologies has raised significant concerns about copyright infringement, data provenance, and the spread of misinformation through deepfake audio. Watermarking offers a proactive solution by embedding imperceptible, identifiable, and traceable marks into audio content. While recent neural network-based watermarking methods like WavMark and AudioSeal have improved robustness and quality, they struggle to achieve both robust detection and accurate attribution simultaneously. This paper introduces the Cross-Attention Robust Audio Watermark (XAttnMark), which bridges this gap by leveraging partial parameter sharing between the generator and the detector, a cross-attention mechanism for efficient message retrieval, and a temporal conditioning module for improved message distribution. Additionally, we propose a psychoacoustic-aligned temporal-frequency masking loss that captures fine-grained auditory masking effects, enhancing watermark imperceptibility. Our approach achieves state-of-the-art performance in both detection and attribution, demonstrating superior robustness against a wide range of audio transformations, including challenging generative editing with strong editing strength. This work represents a significant step forward in protecting intellectual property and ensuring the authenticity of audio content in the era of generative AI.
|
Audio Watermarking, Source Attribution, Watermark Robustness
|
We propose XAttnMark, a cross-attention-based audio watermarking system that achieves robust detection and accurate attribution, guided by a psychoacoustic-aligned temporal-frequency masking loss.
| 561
|
2502.04230
|
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] | null |
2,960
|
Stable Fair Graph Representation Learning with Lipschitz Constraint
|
https://openreview.net/forum?id=oJQWvsStNh
|
[
"Qiang Chen",
"Zhongze Wu",
"Xiu Su",
"Xi Lin",
"Zhe Qu",
"Shan You",
"Shuo Yang",
"Chang Xu"
] |
Poster
|
Group fairness based on adversarial training has gained significant attention on graph data, which was implemented by masking sensitive attributes to generate fair feature views. However, existing models suffer from training instability due to uncertainty of the generated masks and the trade-off between fairness and utility. In this work, we propose a stable fair Graph Neural Network (SFG) to maintain training stability while preserving accuracy and fairness performance. Specifically, we first theoretically derive a tight upper Lipschitz bound to control the stability of existing adversarial-based models and employ a stochastic projected subgradient algorithm to constrain the bound, which operates in a block-coordinate manner. Additionally, we construct the uncertainty set to train the model, which can prevent unstable training by dropping some overfitting nodes caused by chasing fairness. Extensive experiments conducted on three real-world datasets demonstrate that SFG is stable and outperforms other state-of-the-art adversarial-based methods in terms of both fairness and utility performance. Codes are available at https://github.com/sh-qiangchen/SFG.
|
graph neural network, fairness, stability
|
We address training instability in fair GNNs by introducing Lipschitz constraint and distributionally robust optimization, leading to more stable and reliable model performance.
| 555
| null |
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] |
https://github.com/sh-qiangchen/SFG
|
2,961
|
Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal
|
https://openreview.net/forum?id=AyHefRhiZR
|
[
"Deheng Yuan",
"Tao Guo",
"Zhongyi Huang"
] |
Poster
|
Consider the communication-constrained problem of nonparametric function estimation, in which each distributed terminal holds multiple i.i.d. samples. Under certain regularity assumptions, we characterize the minimax optimal rates for all regimes, and identify phase transitions of the optimal rates as the samples per terminal vary from sparse to dense. This fully solves the problem left open by previous works, whose scopes are limited to regimes with either dense samples or a single sample per terminal. To achieve the optimal rates, we design a layered estimation protocol by exploiting protocols for the parametric density estimation problem. We show the optimality of the protocol using information-theoretic methods and strong data processing inequalities, and incorporating the classic balls and bins model. The optimal rates are immediate for various special cases such as density estimation, Gaussian, binary, Poisson and heteroskedastic regression models.
|
Distributed statistical estimation, nonparametric estimation, communication constraints, distributed algorithms, optimal rate of convergence.
| null | 553
|
2501.07879
|
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] | null |
2,962
|
Learning multivariate Gaussians with imperfect advice
|
https://openreview.net/forum?id=0OVCcpLNGI
|
[
"Arnab Bhattacharyya",
"Davin Choo",
"Philips George John",
"Themis Gouleakis"
] |
Poster
|
We revisit the problem of distribution learning within the framework of learning-augmented algorithms.
In this setting, we explore the scenario where a probability distribution is provided as potentially inaccurate advice on the true, unknown distribution. Our objective is to develop learning algorithms whose sample complexity decreases as the quality of the advice improves, thereby surpassing standard learning lower bounds when the advice is sufficiently accurate. Specifically, we demonstrate that this outcome is achievable for the problem of learning a multivariate Gaussian distribution $N(\mu, \Sigma)$ in the PAC learning setting. Classically, in the advice-free setting, $\widetilde{\Theta}(d^2/\varepsilon^2)$ samples are sufficient and worst case necessary to learn $d$-dimensional Gaussians up to TV distance $\varepsilon$ with constant probability. When we are additionally given a parameter $\widetilde{\Sigma}$ as advice, we show that $\widetilde{\mathcal{O}}(d^{2-\beta}/\varepsilon^2)$ samples suffices whenever $|| \widetilde{\Sigma}^{-1/2} \Sigma \widetilde{\Sigma}^{-1/2} - I_d ||_1 \leq \varepsilon d^{1-\beta}$ (where $||\cdot||_1$ denotes the entrywise $\ell_1$ norm) for any $\beta > 0$, yielding a polynomial improvement over the advice-free setting.
|
learning-augmented algorithms, multivariate gaussian learning, sample complexity
|
You can learn gaussians using strictly fewer samples than needed in the worst case, if you start with a good enough guess for the parameters.
| 550
|
2411.12700
|
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] |
https://github.com/philips-george/gaussian-learning-with-advice
|
2,963
|
"Why Is There a Tumor?": Tell Me the Reason, Show Me the Evidence
|
https://openreview.net/forum?id=r3ZLefVUMO
|
[
"Mengmeng Ma",
"Tang Li",
"Yunxiang Peng",
"Lu Lin",
"Volkan Beylergil",
"Binsheng Zhao",
"Oguz Akin",
"Xi Peng"
] |
Poster
|
Medical AI models excel at tumor detection and segmentation. However, their latent representations often lack explicit ties to clinical semantics, producing outputs less trusted in clinical practice. Most of the existing models generate either segmentation masks/labels (localizing where without why) or textual justifications (explaining why without where), failing to ground clinical concepts in spatially localized evidence. To bridge this gap, we propose to develop models that can justify the segmentation or detection using clinically relevant terms and point to visual evidence. We address two core challenges: First, we curate a rationale dataset to tackle the lack of paired images, annotations, and textual rationales for training. The dataset includes 180K image-mask-rationale triples with quality evaluated by expert radiologists. Second, we design rationale-informed optimization that disentangles and localizes fine-grained clinical concepts in a self-supervised manner without requiring pixel-level concept annotations. Experiments across medical benchmarks show our model demonstrates superior performance in segmentation, detection, and beyond. The anonymous link to our code.
|
Trustworthy Medical Image Analysis
| null | 548
| null |
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] |
https://github.com/deep-real/MedRationale
|
2,964
|
Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation
|
https://openreview.net/forum?id=Wz2T778EKJ
|
[
"Zecheng Hao",
"Qichao Ma",
"Kang Chen",
"Yi Zhang",
"Zhaofei Yu",
"Tiejun Huang"
] |
Poster
|
Spiking Neural Network (SNN), as a brain-inspired and energy-efficient network, is currently facing the pivotal challenge of exploring a suitable and efficient learning framework. The predominant training methodologies, namely Spatial-Temporal Back-propagation (STBP) and ANN-SNN Conversion, are encumbered by substantial training overhead or pronounced inference latency, which impedes the advancement of SNNs in scaling to larger networks and navigating intricate application domains. In this work, we propose a novel parallel conversion learning framework, which establishes a mathematical mapping relationship between each time-step of the parallel spiking neurons and the cumulative spike firing rate. We theoretically validate the lossless and sorting properties of the conversion process, as well as pointing out the optimal shifting distance for each step. Furthermore, by integrating the above framework with the distribution-aware error calibration technique, we can achieve efficient conversion towards more general activation functions or training-free circumstance. Extensive experiments have confirmed the significant performance advantages of our method for various conversion cases under ultra-low time latency. To our best knowledge, this is the first work which jointly utilizes parallel spiking calculation and ANN-SNN Conversion, providing a highly promising approach for SNN supervised training. Code is available at https://github.com/hzc1208/Parallel_Conversion.
|
ANN-SNN Conversion, Parallel Spiking Calculation, Distribution-Aware Error Calibration
| null | 537
|
2412.13610
|
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] |
https://github.com/hzc1208/Parallel_Conversion
|
2,965
|
Efficient Motion Prompt Learning for Robust Visual Tracking
|
https://openreview.net/forum?id=aDVzd958YY
|
[
"Jie Zhao",
"Xin Chen",
"Yongsheng Yuan",
"Michael Felsberg",
"Dong Wang",
"Huchuan Lu"
] |
Poster
|
Due to the challenges of processing temporal information, most trackers depend solely on visual discriminability and overlook the unique temporal coherence of video data. In this paper, we propose a lightweight and plug-and-play motion prompt tracking method. It can be easily integrated into existing vision-based trackers to build a joint tracking framework leveraging both motion and vision cues, thereby achieving robust tracking through efficient prompt learning. A motion encoder with three different positional encodings is proposed to encode the long-term motion trajectory into the visual embedding space, while a fusion decoder and an adaptive weight mechanism are designed to dynamically fuse visual and motion features. We integrate our motion module into three different trackers with five models in total. Experiments on seven challenging tracking benchmarks demonstrate that the proposed motion module significantly improves the robustness of vision-based trackers, with minimal training costs and negligible speed sacrifice. Code is available at https://github.com/zj5559/Motion-Prompt-Tracking.
|
visual object tracking, temporal encoding, prompt learning
| null | 536
|
2505.16321
|
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] |
https://github.com/zj5559/Motion-Prompt-Tracking
|
2,966
|
Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding
|
https://openreview.net/forum?id=1SMcxxQiSL
|
[
"Mingyu Jin",
"Kai Mei",
"Wujiang Xu",
"Mingjie Sun",
"Ruixiang Tang",
"Mengnan Du",
"Zirui Liu",
"Yongfeng Zhang"
] |
Poster
|
Large language models (LLMs) have achieved remarkable success in contextual knowledge understanding. In this paper, we show for the first time that these concentrated massive values consistently emerge in specific regions of attention queries (Q) and keys (K) while not having such patterns in values (V) in various modern transformer-based LLMs. Through extensive experiments, we further demonstrate that these massive values play a critical role in interpreting contextual knowledge (i.e., knowledge obtained from the current context window) rather than in retrieving parametric knowledge stored within the model’s parameters. Our further investigation of quantization strategies reveals that ignoring these massive values leads to a pronounced drop in performance on tasks requiring rich contextual understanding, aligning with our analysis. Finally, we trace the emergence of concentrated massive values and find that such concentration is caused by Rotary Positional Encoding (RoPE) and it appears since very first layers. These findings shed new light on how Q and K operate in LLMs and offer practical insights for model design and optimization. The code is available at https://github.com/MingyuJ666/Rope_with_LLM.
|
Massive Value, LLM, Contextual Knowledge Understanding
|
LLMs' attention layers exhibit concentrated massive values in Q and K (but not V) due to RoPE, which proves crucial for contextual knowledge understanding rather than parametric knowledge retrieval.
| 521
|
2502.01563
|
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] |
https://github.com/mingyuj666/rope_with_llm
|
2,967
|
Radio: Rate–Distortion Optimization for Large Language Model Compression
|
https://openreview.net/forum?id=ifnxXCCEiM
|
[
"Sean I. Young"
] |
Poster
|
In recent years, the compression of large language models (LLMs) has emerged as a key problem in facilitating LLM deployment on resource-limited devices, reducing compute costs, and mitigating the environmental footprint due to large-scale AI infrastructure. Here, we establish the foundations of LLM quantization from a rate–distortion theory perspective and propose a quantization technique based on simple rate–distortion optimization. Our technique scales to models containing hundreds of billions of weight parameters and offers users the flexibility to compress models, post-training, to a model size or accuracy specified by the user.
|
rate–distortion theory, optimization, compression, quantization
|
A rate distortion-theoretic framework leads to improved LLM compression outcomes.
| 508
|
2505.03031
|
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] |
https://github.com/seannz/radio
|
2,968
|
Falcon: Fast Visuomotor Policies via Partial Denoising
|
https://openreview.net/forum?id=fiv2M4P5vk
|
[
"Haojun Chen",
"Minghao Liu",
"Chengdong Ma",
"Xiaojian Ma",
"Zailin Ma",
"Huimin Wu",
"Yuanpei Chen",
"Yifan Zhong",
"Mingzhi Wang",
"Qing Li",
"Yaodong Yang"
] |
Poster
|
Diffusion policies are widely adopted in complex visuomotor tasks for their ability to capture multimodal action distributions. However, the multiple sampling steps required for action generation significantly harm real-time inference efficiency, which limits their applicability in real-time decision-making scenarios. Existing acceleration techniques either require retraining or degrade performance under low sampling steps. Here we propose Falcon, which mitigates this speed-performance trade-off and achieves further acceleration. The core insight is that visuomotor tasks exhibit sequential dependencies between actions. Falcon leverages this by reusing partially denoised actions from historical information rather than sampling from Gaussian noise at each step. By integrating current observations, Falcon reduces sampling steps while preserving performance. Importantly, Falcon is a training-free algorithm that can be applied as a plug-in to further improve decision efficiency on top of existing acceleration techniques. We validated Falcon in 48 simulated environments and 2 real-world robot experiments. demonstrating a 2-7x speedup with negligible performance degradation, offering a promising direction for efficient visuomotor policy design.
|
Diffusion Models, Acceleration, Sampling, Robotics, Motion Planning
| null | 506
|
2503.00339
|
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] |
https://github.com/chjchjchjchjchj/Falcon
|
2,969
|
CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty
|
https://openreview.net/forum?id=v7ebdKPTnG
|
[
"Harry Zhang",
"Luca Carlone"
] |
Poster
|
We introduce CUPS, a novel method for learning sequence-to-sequence 3D human shapes and poses from RGB videos with uncertainty quantification. To improve on top of prior work, we develop a method to {generate and score multiple hypotheses during training}, effectively integrating uncertainty quantification into the learning process. This process results in a deep uncertainty function that is trained end-to-end with the 3D pose estimator. Post-training, the learned deep uncertainty model is used as the conformity score, which can be used to calibrate a conformal predictor in order to {assess} the quality of the output prediction. Since the data in human pose-shape learning is not fully exchangeable, we also {present} two practical bounds for the coverage gap in conformal prediction, developing theoretical backing for the uncertainty bound of our model. Our results indicate that
by taking advantage of deep uncertainty with conformal prediction, our method achieves state-of-the-art performance across various
metrics and datasets while inheriting the probabilistic guarantees of conformal prediction. Interactive 3D visualization, code, and data will be available at https://sites.google.com/view/champpp.
|
3D Human Pose-Shape Learning with Uncertainty
| null | 504
|
2412.10431
|
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] | null |
2,970
|
MIPT: Multilevel Informed Prompt Tuning for Robust Molecular Property Prediction
|
https://openreview.net/forum?id=fwIJJGhnBD
|
[
"yeyunchen",
"Jiangming Shi"
] |
Poster
|
The progress in materials science and drug discovery is impeded by the availability of labeled data and the high costs of manual annotation, driving the need for efficient strategies to capture molecular representations and enable accurate predictions. Pretrained Graph Neural Networks have shown promise in capturing universal molecular representations, but adapting them to task-specific applications remains challenging. In this paper, we propose Multilevel Informed Prompt-Tuning (MIPT), a novel framework for effectively tailoring pretrained models to molecule-related tasks. MIPT utilizes a lightweight, multi-level prompt learning module to capture node-level and graph-level task-specific knowledge, ensuring adaptable and efficient tuning. Additionally, a noise penalty mechanism is introduced to address mismatches between pretrained representations and downstream tasks, reducing irrelevant or noisy information. Experimental results show that MIPT surpasses all baselines, aligning graph space and task space while achieving significant improvements in molecule-related tasks, demonstrating its scalability and versatility for molecular tasks.
|
graph prompt, molecule property prediction
| null | 495
| null |
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] | null |
2,971
|
Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment
|
https://openreview.net/forum?id=RP8K523PyW
|
[
"Yu Zhu",
"Chunfeng Song",
"Wanli Ouyang",
"Shan Yu",
"Tiejun Huang"
] |
Poster
|
Individual brains exhibit striking structural and physiological heterogeneity, yet neural circuits can generate remarkably consistent functional properties across individuals, an apparent paradox in neuroscience. While recent studies have observed preserved neural representations in motor cortex through manual alignment across subjects, the zero-shot validation of such preservation and its generalization to more cortices remain unexplored. Here we present PNBA (Probabilistic Neural-Behavioral Representation Alignment), a new framework that leverages probabilistic modeling to address hierarchical variability across trials, sessions, and subjects, with generative constraints preventing representation degeneration. By establishing reliable cross-modal representational alignment, PNBA reveals robust preserved neural representations in monkey primary motor cortex (M1) and dorsal premotor cortex (PMd) through zero-shot validation. We further establish similar representational preservation in mouse primary visual cortex (V1), reflecting a general neural basis. These findings resolve the paradox of neural heterogeneity by establishing zero-shot preserved neural representations across cortices and species, enriching neural coding insights and enabling zero-shot behavior decoding.
|
Neural Representational Consisitency, Motor Cortex, Visual Cortex, Zero-shot, Brain-Computer Interface
|
We developed PNBA, a representation alignment framework that reveals preserved neural representations despite neural heterogeneity in multiple cortices via zero-shot validation, showcasing its practicality through zero-shot behavior decoding.
| 493
|
2505.04331
|
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] |
https://github.com/zhuyu-cs/PNBA
|
2,972
|
KAN-AD: Time Series Anomaly Detection with Kolmogorov–Arnold Networks
|
https://openreview.net/forum?id=LWQ4zu9SdQ
|
[
"Quan Zhou",
"Changhua Pei",
"Fei Sun",
"HanJing",
"Zhengwei Gao",
"Haiming Zhang",
"Gaogang Xie",
"Dan Pei",
"Jianhui li"
] |
Poster
|
Time series anomaly detection (TSAD) underpins real-time monitoring in cloud services and web systems, allowing rapid identification of anomalies to prevent costly failures. Most TSAD methods driven by forecasting models tend to overfit by emphasizing minor fluctuations. Our analysis reveals that effective TSAD should focus on modeling "normal" behavior through smooth local patterns. To achieve this, we reformulate time series modeling as approximating the series with smooth univariate functions. The local smoothness of each univariate function ensures that the fitted time series remains resilient against local disturbances. However, a direct KAN implementation proves susceptible to these disturbances due to the inherently localized characteristics of B-spline functions. We thus propose KAN-AD, replacing B-splines with truncated Fourier expansions and introducing a novel lightweight learning mechanism that emphasizes global patterns while staying robust to local disturbances. On four popular TSAD benchmarks, KAN-AD achieves an average 15% improvement in detection accuracy (with peaks exceeding 27%) over state-of-the-art baselines. Remarkably, it requires fewer than 1,000 trainable parameters, resulting in a 50% faster inference speed compared to the original KAN, demonstrating the approach's efficiency and practical viability.
|
anomaly detection, time series analysis
| null | 492
| null |
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] |
https://github.com/issaccv/KAN-AD
|
2,973
|
Quantum Algorithms for Finite-horizon Markov Decision Processes
|
https://openreview.net/forum?id=toTYcYf4qI
|
[
"Bin Luo",
"Yuwen Huang",
"Jonathan Allcock",
"Xiaojun Lin",
"Shengyu Zhang",
"John C.S. Lui"
] |
Poster
|
In this work, we design quantum algorithms that are more efficient than classical algorithms to solve time-dependent and finite-horizon Markov Decision Processes (MDPs) in two distinct settings: (1) In the exact dynamics setting, where the agent has full knowledge of the environment's dynamics (i.e., transition probabilities), we prove that our **Quantum Value Iteration (QVI)** algorithm **QVI-1** achieves a quadratic speedup in the size of the action space $(A)$ compared with the classical value iteration algorithm for computing the optimal policy ($\pi^{\ast}$) and the optimal V-value function ($V_{0}^{\ast}$). Furthermore, our algorithm **QVI-2** provides an additional speedup in the size of the state space $(S)$ when obtaining near-optimal policies and V-value functions. Both **QVI-1** and **QVI-2** achieve quantum query complexities that provably improve upon classical lower bounds, particularly in their dependences on $S$ and $A$. (2) In the generative model setting, where samples from the environment are accessible in quantum superposition, we prove that our algorithms **QVI-3** and **QVI-4** achieve improvements in sample complexity over the state-of-the-art (SOTA) classical algorithm in terms of $A$, estimation error $(\epsilon)$, and time horizon $(H)$. More importantly, we prove quantum lower bounds to show that **QVI-3** and **QVI-4** are asymptotically optimal, up to logarithmic factors, assuming a constant time horizon.
|
Quantum algorithms, Markov decision processes, lower bounds
|
We design quantum algorithms that solve finite-horizon Markov decision process problems more efficiently than classical algorithms and establish quantum lower bounds to rule out the possibility of exponential quantum speedups.
| 459
| null |
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] | null |
2,974
|
Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning
|
https://openreview.net/forum?id=BMJ3pyYxu2
|
[
"Zhenni Bi",
"Kai Han",
"Chuanjian Liu",
"Yehui Tang",
"Yunhe Wang"
] |
Poster
|
Large Language Models (LLMs) have demonstrated remarkable abilities across various language tasks, but solving complex reasoning problems remains a significant challenge. While existing methods, such as Chain-of-Thought (CoT) and Tree-of-Thought (ToT), enhance reasoning by decomposing problems or structuring prompts, they typically perform a single pass of reasoning and may fail to revisit flawed paths, compromising accuracy. To address this limitation, we propose a novel reasoning framework called Forest-of-Thought (FoT), which integrates multiple reasoning trees to leverage collective decision-making for solving complex logical problems. FoT employs sparse activation strategies to select the most relevant reasoning paths, improving both efficiency and accuracy. Additionally, we introduce a dynamic self-correction strategy that enables real-time error correction, along with consensus-guided decision-making strategies to optimize both correctness and computational resources. Experimental results demonstrate that the FoT framework, combined with these strategies, significantly enhances the reasoning capabilities of LLMs, enabling them to solve complex tasks with greater precision and efficiency.
|
forest of thought; fot; nlp; llm
| null | 445
| null |
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] |
https://github.com/iamhankai/Forest-of-Thought
|
2,975
|
Matryoshka Quantization
|
https://openreview.net/forum?id=phVWcUSGYP
|
[
"Pranav Ajit Nair",
"Puranjay Datta",
"Jeff Dean",
"Prateek Jain",
"Aditya Kusupati"
] |
Poster
|
Quantizing model weights is critical for reducing
the communication and inference costs of large
models. However, quantizing models – especially
to low precisions like int4 or int2 – requires a
trade-off in model quality; int2, in particular, is
known to severely degrade model quality. Consequently, practitioners are often forced to maintain
multiple models with different quantization levels or serve a single model that best satisfies the
quality-latency trade-off. On the other hand, integer data types, such as int8, inherently possess
a nested (Matryoshka) structure where smaller
bit-width integers, like int4 or int2, are nested
within the most significant bits. Leveraging this
insight, in this paper, we propose Matryoshka
Quantization (MatQuant), a novel multi-scale
quantization technique that alleviates the aforementioned challenge. This technique allows us to
train and maintain a single quantized model but
serve it with the precision demanded by the deployment. Furthermore, leveraging MatQuant’s
co-training and co-distillation, int2 precision models extracted by MatQuant outperform standard
int2 quantization by up to 4% and 7% with OmniQuant and QAT as base algorithms respectively.
Finally, we demonstrate that by using an extra bit
to represent outliers, a model with an effective
precision of 2.05-bit improves further by 6% with
OmniQuant as the base algorithm.
|
Matryoshka, Quantization, LLMs, Deployment
|
Matryoshka Quantization (MatQuant) trains a single model that can operate at multiple bitwidths (e.g. Int8, Int4, Int2) simultaneously by leveraging the nested structure of integer data types, outperforming independent quantization of the same models
| 442
|
2502.06786
|
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] | null |
2,976
|
FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
|
https://openreview.net/forum?id=JFafMSAjUm
|
[
"Yingying Deng",
"Xiangyu He",
"Changwang Mei",
"Peisong Wang",
"Fan Tang"
] |
Poster
|
Though Rectified Flows (ReFlows) with distillation offer a promising way for fast sampling, its fast inversion transforms images back to structured noise for recovery and following editing remains unsolved. This paper introduces FireFlow, an embarrassingly simple yet effective zero-shot approach that inherits the startling capacity of ReFlow-based models (such as FLUX) in generation while extending its capabilities to accurate inversion and editing in **8** steps.
We first demonstrate that a carefully designed numerical solver is pivotal for ReFlow inversion, enabling accurate inversion and reconstruction with the precision of a second-order solver while maintaining the practical efficiency of a first-order Euler method. This solver achieves a $3\times$ runtime speedup compared to state-of-the-art ReFlow inversion and editing techniques while delivering smaller reconstruction errors and superior editing results in a training-free mode. The code is available at [this-URL](https://github.com/HolmesShuan/FireFlow-Fast-Inversion-of-Rectified-Flow-for-Image-Semantic-Editing).
|
Rectified Flow, Image Editing
|
An 8-step inversion and 8-step editing process works effectively with the FLUX-dev model. (3x speedup with results that are comparable or even superior to baseline methods)
| 435
|
2412.07517
|
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] |
https://github.com/HolmesShuan/FireFlow-Fast-Inversion-of-Rectified-Flow-for-Image-Semantic-Editing
|
2,977
|
RelGNN: Composite Message Passing for Relational Deep Learning
|
https://openreview.net/forum?id=XXh3zmw2Uy
|
[
"Tianlang Chen",
"Charilaos Kanatsoulis",
"Jure Leskovec"
] |
Poster
|
Predictive tasks on relational databases are critical in real-world applications spanning e-commerce, healthcare, and social media. To address these tasks effectively, Relational Deep Learning (RDL) encodes relational data as graphs, enabling Graph Neural Networks (GNNs) to exploit relational structures for improved predictions. However, existing RDL methods often overlook the intrinsic structural properties of the graphs built from relational databases, leading to modeling inefficiencies, particularly in handling many-to-many relationships. Here we introduce RelGNN, a novel GNN framework specifically designed to leverage the unique structural characteristics of the graphs built from relational databases. At the core of our approach is the introduction of atomic routes, which are simple paths that enable direct single-hop interactions between the source and destination nodes. Building upon these atomic routes, RelGNN designs new composite message passing and graph attention mechanisms that reduce redundancy, highlight key signals, and enhance predictive accuracy. RelGNN is evaluated on 30 diverse real-world tasks from Relbench (Fey et al., 2024), and achieves state-of-the-art performance on the vast majority of tasks, with improvements of up to 25%.
|
Graph Neural Networks, Relational Deep Learning
|
RelGNN is a novel GNN framework that introduces composite message passing and graph attention mechanisms based on automatically derived "atomic routes" to efficiently model relational data, achieving state-of-the-art performance on Relbench.
| 431
|
2502.06784
|
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] |
https://github.com/snap-stanford/RelGNN
|
2,978
|
R2-T2: Re-Routing in Test-Time for Multimodal Mixture-of-Experts
|
https://openreview.net/forum?id=oqPcOMafOF
|
[
"Zhongyang Li",
"Ziyue Li",
"Tianyi Zhou"
] |
Poster
|
In large multimodal models (LMMs), the perception of non-language modalities (e.g., visual representations) is usually not on par with the large language models (LLMs)' powerful reasoning capabilities, deterring LMMs' performance on challenging downstream tasks.
This weakness has been recently mitigated by replacing the vision encoder with a mixture-of-experts (MoE), which provides rich, multi-granularity, and diverse representations required by different downstream tasks. The performance of multimodal MoE largely depends on its router, which reweights and mixes the representations of different experts for each input. However, we find that the end-to-end trained router does not always produce the optimal routing weights for every test sample. To bridge the gap, we propose a novel and efficient method ''**R**e-**R**outing in **T**est-**T**ime (R2-T2)'' that locally optimizes the vector of routing weights in test-time by moving it toward those vectors of the correctly predicted samples in a neighborhood of the test sample. We propose three R2-T2 strategies with different optimization objectives and neighbor-search spaces. R2-T2 consistently and significantly improves state-of-the-art LMMs' performance on challenging multimodal benchmarks of diverse tasks, without training any parameters in the base model. Our code can be accessed here.
|
mixture-of-experts, test-time optimization, multimodal models
|
A test-time method that dynamically adjusts Mixture-of-Experts routing weights to boost multimodal model performance without any retraining
| 411
|
2502.20395
|
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] |
https://github.com/tianyi-lab/R2-T2
|
2,979
|
When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need
|
https://openreview.net/forum?id=pekYL20W3n
|
[
"Ziming Hong",
"Runnan Chen",
"Zengmao Wang",
"Bo Han",
"Bo Du",
"Tongliang Liu"
] |
Poster
|
Data-free knowledge distillation (DFKD) transfers knowledge from a teacher to a student without access the real in-distribution (ID) data. Its common solution is to use a generator to synthesize fake data and use them as a substitute for real ID data. However, existing works typically assume teachers are trustworthy, leaving the robustness and security of DFKD from untrusted teachers largely unexplored. In this work, we conduct the first investigation into distilling non-transferable learning (NTL) teachers using DFKD, where the transferability from an ID domain to an out-of-distribution (OOD) domain is prohibited. We find that NTL teachers fool DFKD through divert the generator's attention from the useful ID knowledge to the misleading OOD knowledge. This hinders ID knowledge transfer but prioritizes OOD knowledge transfer. To mitigate this issue, we propose Adversarial Trap Escaping (ATEsc) to benefit DFKD by identifying and filtering out OOD-like synthetic samples. Specifically, inspired by the evidence that NTL teachers show stronger adversarial robustness on OOD samples than ID samples, we split synthetic samples into two groups according to their robustness. The fragile group is treated as ID-like data and used for normal knowledge distillation, while the robust group is seen as OOD-like data and utilized for forgetting OOD knowledge. Extensive experiments demonstrate the effectiveness of ATEsc for improving DFKD against NTL teachers.
|
Data-free Knowledge Distillation, Non-Transferable Learning
| null | 408
|
2507.04119
|
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] |
https://github.com/tmllab/2025_ICML_ATEsc
|
2,980
|
Deliberation in Latent Space via Differentiable Cache Augmentation
|
https://openreview.net/forum?id=IaUJl5RCOu
|
[
"Luyang Liu",
"Jonas Pfeiffer",
"Jiaxing Wu",
"Jun Xie",
"Arthur Szlam"
] |
Poster
|
Techniques enabling large language models (LLMs) to "think more" by generating and attending to intermediate reasoning steps have shown promise in solving complex problems. However, the standard approaches generate sequences of discrete tokens immediately before responding, and so they can incur significant latency costs and be challenging to optimize. In this work, we demonstrate that a frozen LLM can be augmented with an offline coprocessor that operates on the model's key-value (kv) cache. This coprocessor augments the cache with a set of latent embeddings designed to improve the fidelity of subsequent decoding. We train this coprocessor using the language modeling loss from the decoder on standard pretraining data, while keeping the decoder itself frozen. This approach enables the model to learn, in an end-to-end differentiable fashion, how to distill additional computation into its kv-cache. Because the decoder remains unchanged, the coprocessor can operate offline and asynchronously, and the language model can function normally if the coprocessor is unavailable or if a given cache is deemed not to require extra computation. We show experimentally that when a cache is augmented, the decoder achieves lower perplexity on numerous subsequent tokens. Furthermore, even without any task-specific training, our experiments demonstrate that cache augmentation consistently improves performance across a range of reasoning-intensive tasks.
|
Latent reasoning, Cache augmentation, LLM
|
This paper introduces a method to improve LLM reasoning by using a separate "coprocessor" to add learned latent information to the LLM's KV-cache without changing the original LLM, leading to better performance on complex tasks.
| 403
|
2412.17747
|
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] | null |
2,981
|
Test-Time Canonicalization by Foundation Models for Robust Perception
|
https://openreview.net/forum?id=JMZ7mr19AK
|
[
"Utkarsh Singhal",
"Ryan Feng",
"Stella X. Yu",
"Atul Prakash"
] |
Poster
|
Real-world visual perception requires invariance to diverse transformations, yet current methods rely heavily on specialized architectures or training on predefined augmentations, limiting generalization. We propose FoCal, a test-time, data-driven framework that achieves robust perception by leveraging internet-scale visual priors from foundation models. By generating and optimizing candidate transformations toward visually typical, "canonical" views, FoCal enhances robustness without retraining or architectural changes. Experiments demonstrate improved robustness of CLIP and SAM across challenging transformations, including 2D/3D rotations, illumination shifts (contrast and color), and day-night variations. We also highlight potential applications in active vision. Our approach challenges the assumption that transform-specific training is necessary, instead offering a scalable path to invariance. Our code is available at: https://github.com/sutkarsh/focal.
|
equivariance, invariance, CLIP, diffusion, canonicalization, ecological, unsupervised, test-time scaling
|
Invariance to diverse and complex transformations by leveraging knowledge in vision foundation models.
| 402
|
2507.10375
|
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] |
https://github.com/sutkarsh/focal
|
2,982
|
A Parametric Contextual Online Learning Theory of Brokerage
|
https://openreview.net/forum?id=GA7RDBGs6S
|
[
"François Bachoc",
"Tommaso Cesari",
"Roberto Colomboni"
] |
Poster
|
We study the role of contextual information in the online learning problem of brokerage between traders.
In this sequential problem, at each time step, two traders arrive with secret valuations about an asset they wish to trade.
The learner (a broker) suggests a trading (or brokerage) price based on contextual data about the asset and the market conditions.
Then, the traders reveal their willingness to buy or sell based on whether their valuations are higher or lower than the brokerage price.
A trade occurs if one of the two traders decides to buy and the other to sell, i.e., if the broker's proposed price falls between the smallest and the largest of their two valuations.
We design algorithms for this problem and prove optimal theoretical regret guarantees under various standard assumptions.
|
bilateral trade, regret minimization, contextual online learning, theory
|
We study a contextual version of online learning for brokerage
| 401
| null |
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] | null |
2,983
|
No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces
|
https://openreview.net/forum?id=RBZpAa27ls
|
[
"Daniel Marczak",
"Simone Magistri",
"Sebastian Cygert",
"Bartłomiej Twardowski",
"Andrew D. Bagdanov",
"Joost van de Weijer"
] |
Poster
|
Model merging integrates the weights of multiple task-specific models into a single multi-task model. Despite recent interest in the problem, a significant performance gap between the combined and single-task models remains. In this paper, we investigate the key characteristics of task matrices -- weight update matrices applied to a pre-trained model -- that enable effective merging. We show that alignment between singular components of task-specific and merged matrices strongly correlates with performance improvement over the pre-trained model. Based on this, we propose an isotropic merging framework that flattens the singular value spectrum of task matrices, enhances alignment, and reduces the performance gap. Additionally, we incorporate both common and task-specific subspaces to further improve alignment and performance. Our proposed approach achieves state-of-the-art performance on vision and language tasks across various sets of tasks and model scales. This work advances the understanding of model merging dynamics, offering an effective methodology to merge models without requiring additional training.
|
Model merging
|
We achieve state-of-the-art model merging results by proposing isotropic merging in common and task-specific subspaces of weight update matrices across vision and language, for fully fine-tuned and LoRA-adapted models
| 399
|
2502.04959
|
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] |
https://github.com/danielm1405/iso-merging
|
2,984
|
CellFlux: Simulating Cellular Morphology Changes via Flow Matching
|
https://openreview.net/forum?id=3NLNmdheIi
|
[
"Yuhui Zhang",
"Yuchang Su",
"Chenyu Wang",
"Tianhong Li",
"Zoe Wefers",
"Jeffrey J Nirschl",
"James Burgess",
"Daisy Ding",
"Alejandro Lozano",
"Emma Lundberg",
"Serena Yeung-Levy"
] |
Poster
|
Building a virtual cell capable of accurately simulating cellular behaviors in silico has long been a dream in computational biology. We introduce CellFlux, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations using flow matching. Unlike prior methods, CellFlux models distribution-wise transformations from unperturbed to perturbed cell states, effectively distinguishing actual perturbation effects from experimental artifacts such as batch effects—a major challenge in biological data. Evaluated on chemical (BBBC021), genetic (RxRx1), and combined perturbation (JUMP) datasets, CellFlux generates biologically meaningful cell images that faithfully capture perturbation-specific morphological changes, achieving a 35% improvement in FID scores and a 12% increase in mode-of-action prediction accuracy over existing methods. Additionally, CellFlux enables continuous interpolation between cellular states, providing a potential tool for studying perturbation dynamics. These capabilities mark a significant step toward realizing virtual cell modeling for biomedical research. Project page: https://yuhui-zh15.github.io/CellFlux/.
|
flow matching, cell image, drug discovery, generative models
|
CellFlux is an image-generative model that simulates cellular morphology changes from chemical and genetic perturbations using flow matching.
| 393
|
2502.09775
|
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] |
https://github.com/yuhui-zh15/CellFlux
|
2,985
|
Taming Diffusion for Dataset Distillation with High Representativeness
|
https://openreview.net/forum?id=P1RMiAn4Tr
|
[
"Lin Zhao",
"Yushu Wu",
"Xinru Jiang",
"Jianyang Gu",
"Yanzhi Wang",
"Xiaolin Xu",
"Pu Zhao",
"Xue Lin"
] |
Poster
|
Recent deep learning models demand larger datasets, driving the need for dataset distillation to create compact, cost-efficient datasets while maintaining performance. Due to the powerful image generation capability of diffusion, it has been introduced to this field for generating distilled images. In this paper, we systematically investigate issues present in current diffusion-based dataset distillation methods, including inaccurate distribution matching, distribution deviation with random noise, and separate sampling. Building on this, we propose D$^3$HR, a novel diffusion-based framework to generate distilled datasets with high representativeness. Specifically, we adopt DDIM inversion to map the latents of the full dataset from a low-normality latent domain to a high-normality Gaussian domain, preserving information and ensuring structural consistency to generate representative latents for the distilled dataset. Furthermore, we propose an efficient sampling scheme to better align the representative latents with the high-normality Gaussian distribution. Our comprehensive experiments demonstrate that D$^3$HR can achieve higher accuracy across different model architectures compared with state-of-the-art baselines in dataset distillation. Source code: https://github.com/lin-zhao-resoLve/D3HR.
|
dataset distillation
|
A new paradigm for diffusion-based dataset distillation with high representativeness.
| 378
|
2505.18399
|
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] |
https://github.com/lin-zhao-resoLve/D3HR
|
2,986
|
More Than Meets the Eye: Enhancing Multi-Object Tracking Even with Prolonged Occlusions
|
https://openreview.net/forum?id=CVQwQp9V1D
|
[
"Bishoy Galoaa",
"Somaieh Amraee",
"Sarah Ostadabbas"
] |
Poster
|
This paper introduces MOTE (MOre Than meets the Eye), a novel multi-object tracking (MOT) algorithm designed to address the challenges of tracking occluded objects. By integrating deformable detection transformers with a custom disocclusion matrix, MOTE significantly enhances the ability to track objects even when they are temporarily hidden from view. The algorithm leverages optical flow to generate features that are processed through a softmax splatting layer, which aids in the creation of a disocclusion matrix. This matrix plays a crucial role in maintaining track consistency by estimating the motion of occluded objects. MOTE's architecture includes modifications to the enhanced track embedding module (ETEM), which allows it to incorporate these advanced features into the track query layer embeddings. This integration ensures that the model not only tracks visible objects but also accurately predicts the trajectories of occluded ones, much like the human visual system. The proposed method is evaluated on multiple datasets, including MOT17, MOT20, and DanceTrack, where it achieves impressive tracking metrics--82.0 MOTA and 66.3 HOTA on the MOT17 dataset, 81.7 MOTA and 65.8 HOTA on the MOT20 dataset, and 93.2 MOTA and 74.2 HOTA on the DanceTrack dataset. Notably, MOTE excels in reducing identity switches and maintaining consistent tracking in complex real-world scenarios with frequent occlusions, outperforming existing state-of-the-art methods across all tested benchmarks.
|
Multi-object tracking, occlusion handling, deformable transformers, softmax splatting, optical flow estimation, enhanced track embedding, computer vision, motion estimation, deep learning.
|
A novel multi-object tracking framework, MOTE, that uses deformable transformers, optical flow, and softmax splatting to track objects effectively even under prolonged occlusions.
| 360
| null |
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] |
https://github.com/ostadabbas/MOTE-More-Than-Meets-the-Eye-Tracking
|
2,987
|
CPCF: A Cross-Prompt Contrastive Framework for Referring Multimodal Large Language Models
|
https://openreview.net/forum?id=0MpGi6IwZr
|
[
"Lanyun Zhu",
"Deyi Ji",
"Tianrun Chen",
"Haiyang Wu",
"De Wen Soh",
"Jun Liu"
] |
Poster
|
Referring MLLMs extend conventional multimodal large language models by allowing them to receive referring visual prompts and generate responses tailored to the indicated regions. However, these models often suffer from suboptimal performance due to incorrect responses tailored to misleading areas adjacent to or similar to the target region. This work introduces CPCF, a novel framework to address this issue and achieve superior results. CPCF contrasts outputs generated from the indicated visual prompt with those from contrastive prompts sampled from misleading regions, effectively suppressing the influence of erroneous information outside the target region on response generation. To further enhance the effectiveness and efficiency of our framework, several novel designs are proposed, including a prompt extraction network to automatically identify suitable contrastive prompts, a self-training method that leverages unlabeled data to improve training quality, and a distillation approach to reduce the additional computational overhead associated with contrastive decoding. Incorporating these novel designs, CPCF achieves state-of-the-art performance, as demonstrated by extensive experiments across multiple benchmarks. Project page: https://lanyunzhu.site/CPCF/
|
Referring multimodal large language models
| null | 359
| null |
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] | null |
2,988
|
TeLoGraF: Temporal Logic Planning via Graph-encoded Flow Matching
|
https://openreview.net/forum?id=9NmdppWbXi
|
[
"Yue Meng",
"Chuchu Fan"
] |
Poster
|
Learning to solve complex tasks with signal temporal logic (STL) specifications is crucial to many real-world applications. However, most previous works only consider fixed or parametrized STL specifications due to the lack of a diverse STL dataset and encoders to effectively extract temporal logic information for downstream tasks. In this paper, we propose TeLoGraF, Temporal Logic Graph-encoded Flow, which utilizes Graph Neural Networks (GNN) encoder and flow-matching to learn solutions for general STL specifications. We identify four commonly used STL templates and collect a total of 200K specifications with paired demonstrations. We conduct extensive experiments in five simulation environments ranging from simple dynamical models in the 2D space to high-dimensional 7DoF Franka Panda robot arm and Ant quadruped navigation. Results show that our method outperforms other baselines in the STL satisfaction rate. Compared to classical STL planning algorithms, our approach is 10-100X faster in inference and can work on any system dynamics. Besides, we show our graph-encoding method's capability to solve complex STLs and robustness to out-distribution STL specifications. Code is available at https://github.com/mengyuest/TeLoGraF
|
Imitation Learning, Temporal Logic, Flow Matching, Graph Neural Networks
|
We utilize Graph Neural Networks (GNN) encoder and flow-matching to learn solutions for general signal temporal logic specifications.
| 347
|
2505.00562
|
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] |
https://github.com/mengyuest/TeLoGraF
|
2,989
|
PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion
|
https://openreview.net/forum?id=FQoy1Y1Hd8
|
[
"Sophia Tang",
"Yinuo Zhang",
"Pranam Chatterjee"
] |
Poster
|
We present **PepTune**, a multi-objective discrete diffusion model for simultaneous generation and optimization of therapeutic peptide SMILES. Built on the Masked Discrete Language Model (MDLM) framework, PepTune ensures valid peptide structures with a novel bond-dependent masking schedule and invalid loss function. To guide the diffusion process, we introduce **Monte Carlo Tree Guidance (MCTG)**, an inference-time multi-objective guidance algorithm that balances exploration and exploitation to iteratively refine Pareto-optimal sequences. MCTG integrates classifier-based rewards with search-tree expansion, overcoming gradient estimation challenges and data sparsity. Using PepTune, we generate diverse, chemically-modified peptides simultaneously optimized for multiple therapeutic properties, including target binding affinity, membrane permeability, solubility, hemolysis, and non-fouling for various disease-relevant targets. In total, our results demonstrate that MCTG for masked discrete diffusion is a powerful and modular approach for multi-objective sequence design in discrete state spaces.
|
Discrete diffusion, multi-objective optimization, therapeutic peptides
|
PepTune generates therapeutic peptides in discrete space using Monte Carlo Tree Guidance for multi-objective optimization.
| 341
|
2412.17780
|
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] | null |
2,990
|
Asymmetric Decision-Making in Online Knowledge Distillation: Unifying Consensus and Divergence
|
https://openreview.net/forum?id=bf8862KaL1
|
[
"Zhaowei Chen",
"Borui Zhao",
"Yuchen Ge",
"Yuhao Chen",
"Renjie Song",
"Jiajun Liang"
] |
Poster
|
Online Knowledge Distillation (OKD) methods represent a streamlined, one-stage distillation training process that obviates the necessity of transferring knowledge from a pretrained teacher network to a more compact student network. In contrast to existing logits-based OKD methods, this paper presents an innovative approach to leverage intermediate spatial representations. Our analysis of the intermediate features from both teacher and student models reveals two pivotal insights: (1) the similar features between students and teachers are predominantly focused on the foreground objects. (2) teacher models emphasize foreground objects more than students. Building on these findings, we propose Asymmetric Decision-Making (ADM) to enhance feature consensus learning for student models while continuously promoting feature diversity in teacher models. Specifically, Consensus Learning for student models prioritizes spatial features with high consensus relative to teacher models. Conversely, Divergence Learning for teacher models highlights spatial features with lower similarity compared to student models, indicating superior performance by teacher models in these regions. Consequently, ADM facilitates the student models to catch up with the feature learning process of the teacher models. Extensive experiments demonstrate that ADM consistently surpasses existing OKD methods across various online knowledge distillation settings and also achieves superior results when transferred to offline knowledge distillation, semantic segmentation and diffusion distillation tasks.
|
knowledge distillation
| null | 331
|
2503.06685
|
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] | null |
2,991
|
Canonical Rank Adaptation: An Efficient Fine-Tuning Strategy for Vision Transformers
|
https://openreview.net/forum?id=vexHifrbJg
|
[
"Lokesh Veeramacheneni",
"Moritz Wolter",
"Hilde Kuehne",
"Juergen Gall"
] |
Poster
|
Modern methods for fine-tuning a Vision Transformer (ViT) like Low-Rank Adaptation (LoRA) and its variants demonstrate impressive performance. However, these methods ignore the high-dimensional nature of Multi-Head Attention (MHA) weight tensors. To address this limitation, we propose Canonical Rank Adaptation (CaRA). CaRA leverages tensor mathematics, first by tensorising the transformer into two different tensors; one for projection layers in MHA and the other for feed-forward layers. Second, the tensorised formulation is fine-tuned using the low-rank adaptation in Canonical-Polyadic Decomposition (CPD) form. Employing CaRA efficiently minimizes the number of trainable parameters. Experimentally, CaRA outperforms existing Parameter-Efficient Fine-Tuning (PEFT) methods in visual classification benchmarks such as Visual Task Adaptation Benchmark (VTAB)-1k and Fine-Grained Visual Categorization (FGVC).
|
CaRA, Canonical Polyadic Decomposition, CPD, Tensor methods, ViT, LoRA
|
This paper leverages the Canonical Polyadic Decomposition to define a parameter efficient fine tuning process for vision transformers.
| 324
| null |
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] | null |
2,992
|
iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection
|
https://openreview.net/forum?id=hcjXy7kLFd
|
[
"Huahui Yi",
"Wei Xu",
"Ziyuan Qin",
"Xi Chen",
"Xiaohu Wu",
"Kang Li",
"Qicheng Lao"
] |
Poster
|
Existing prompt-based approaches have demonstrated impressive performance in continual learning, leveraging pre-trained large-scale models for classification tasks; however, the tight coupling between foreground-background information and the coupled attention between prompts and image-text tokens present significant challenges in incremental medical object detection tasks, due to the conceptual gap between medical and natural domains. To overcome these challenges, we introduce the iDPA framework, which comprises two main components: 1) Instance-level Prompt Generation (IPG), which decouples fine-grained instance-level knowledge from images and generates prompts that focus on dense predictions, and 2) Decoupled Prompt Attention (DPA), which decouples the original prompt attention, enabling a more direct and efficient transfer of prompt information while reducing memory usage and mitigating catastrophic forgetting. We collect 13 clinical, cross-modal, multi-organ, and multi-category datasets, referred to as ODinM-13, and experiments demonstrate that iDPA outperforms existing SOTA methods, with FAP improvements of f 5.44%, 4.83%, 12.88%, and 4.59% in full data, 1-shot, 10-shot, and 50-shot settings, respectively.
|
Continual Learning.+Medical Object Detection
|
iDPA improves incremental medical object detection by introducing instance-level prompt generation and decoupled prompt attention, achieving superior performance on ODinM-13 across various data settings.
| 313
|
2506.00406
|
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] | null |
2,993
|
Emotional Face-to-Speech
|
https://openreview.net/forum?id=EuDlvBqcSm
|
[
"Jiaxin Ye",
"Boyuan Cao",
"Hongming Shan"
] |
Poster
|
How much can we infer about an emotional voice solely from an expressive face? This intriguing question holds great potential for applications such as virtual character dubbing and aiding individuals with expressive language disorders. Existing face-to-speech methods offer great promise in capturing identity characteristics but struggle to generate diverse vocal styles with emotional expression. In this paper, we explore a new task, termed *emotional face-to-speech*, aiming to synthesize emotional speech directly from expressive facial cues. To that end, we introduce **DEmoFace**, a novel generative framework that leverages a discrete diffusion transformer (DiT) with curriculum learning, built upon a multi-level neural audio codec. Specifically, we propose multimodal DiT blocks to dynamically align text and speech while tailoring vocal styles based on facial emotion and identity. To enhance training efficiency and generation quality, we further introduce a coarse-to-fine curriculum learning algorithm for multi-level token processing. In addition, we develop an enhanced predictor-free guidance to handle diverse conditioning scenarios, enabling multi-conditional generation and disentangling complex attributes effectively. Extensive experimental results demonstrate that DEmoFace generates more natural and consistent speech compared to baselines, even surpassing speech-driven methods. Demos of DEmoFace are shown at our project https://demoface.github.io.
|
Generative Model, Discrete Diffusion Model, Speech Generation, Text-to-Speech
|
We propose a novel RVQ-based discrete diffusion framework DEmoFace for a new task (Emotional Face-to- Speech, eF2S), which is the first attempt to customize consistent vocal style including timbre and emotional prosody solely from the face.
| 311
|
2502.01046
|
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] | null |
2,994
|
Consensus Based Stochastic Optimal Control
|
https://openreview.net/forum?id=W7dN3SQKkH
|
[
"Liyao Lyu",
"Jingrun Chen"
] |
Poster
|
We propose a *gradient-free* deep reinforcement learning algorithm to solve *high-dimensional*, finite-horizon stochastic control problems.
Although the recently developed deep reinforcement learning framework has achieved great success in solving these problems, direct estimation of policy gradients from Monte Carlo sampling often suffers from high variance. To address this, we introduce the Momentum Consensus-Based Optimization (M-CBO) and Adaptive Momentum Consensus-Based Optimization (Adam-CBO) frameworks. These methods optimize policies using Monte Carlo estimates of the value function, rather than its gradients. Adjustable Gaussian noise supports efficient exploration, helping the algorithm converge to optimal policies in complex, nonconvex environments. Numerical results confirm the accuracy and scalability of our approach across various problem dimensions and show the potential for extension to mean-field control problems. Theoretically, we prove that M-CBO can converge to the optimal policy under some assumptions.
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deep reinforcement learning, optimal control, McKean-Vlasov control, consensus-based optimization
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] |
https://github.com/Lyuliyao/ADAM_CBO_control
|
2,995
|
Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent
|
https://openreview.net/forum?id=EqoKRSR5Pa
|
[
"Yongxian Wei",
"Anke Tang",
"Li Shen",
"Zixuan Hu",
"Chun Yuan",
"Xiaochun Cao"
] |
Poster
|
Merging multiple expert models offers a promising approach for performing multi-task learning without accessing their original data. Existing methods attempt to alleviate task conflicts by sparsifying task vectors or promoting orthogonality among them. However, they overlook the fundamental target of model merging: the merged model performs as closely as possible to task-specific models on respective tasks. We find these methods inevitably discard task-specific information that, while causing conflicts, is crucial for performance. Based on our findings, we frame model merging as a constrained optimization problem ($\textit{i.e.}$, minimizing the gap between the merged model and individual models, subject to the constraint of retaining shared knowledge) and solve it via adaptive projective gradient descent. Specifically, we align the merged model with individual models by decomposing and reconstituting the loss function, alleviating conflicts through $\textit{data-free}$ optimization of task vectors. To retain shared knowledge, we optimize this objective by projecting gradients within a $\textit{shared subspace}$ spanning all tasks. Moreover, we view merging coefficients as adaptive learning rates and propose a task-aware, training-free strategy. Experiments show that our plug-and-play approach consistently outperforms previous methods, achieving state-of-the-art results across diverse architectures and tasks in both vision and NLP domains.
|
Model Merging, Task Vector, Gradient Projection
| null | 307
|
2501.01230
|
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] |
https://github.com/walkerworldpeace/doge
|
2,996
|
Risk and cross validation in ridge regression with correlated samples
|
https://openreview.net/forum?id=GMwKpJ9TiR
|
[
"Alexander Atanasov",
"Jacob A Zavatone-Veth",
"Cengiz Pehlevan"
] |
Poster
|
Recent years have seen substantial advances in our understanding of high-dimensional ridge regression, but existing theories assume that training examples are independent. By leveraging techniques from random matrix theory and free probability, we provide sharp asymptotics for the in- and out-of-sample risks of ridge regression when the data points have arbitrary correlations. We demonstrate that in this setting, the generalized cross validation estimator (GCV) fails to correctly predict the out-of-sample risk. However, in the case where the noise residuals have the same correlations as the data points, one can modify the GCV to yield an efficiently-computable unbiased estimator that concentrates in the high-dimensional limit, which we dub CorrGCV. We further extend our asymptotic analysis to the case where the test point has nontrivial correlations with the training set, a setting often encountered in time series forecasting. Assuming knowledge of the correlation structure of the time series, this again yields an extension of the GCV estimator, and sharply characterizes the degree to which such test points yield an overly optimistic prediction of long-time risk. We validate the predictions of our theory across a variety of high dimensional data.
|
Linear regression, random matrix theory
| null | 301
|
2408.04607
|
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] |
https://github.com/Pehlevan-Group/S_transform
|
2,997
|
EraseAnything: Enabling Concept Erasure in Rectified Flow Transformers
|
https://openreview.net/forum?id=vvBAZJh2nQ
|
[
"Daiheng Gao",
"Shilin Lu",
"Wenbo Zhou",
"Jiaming Chu",
"Jie Zhang",
"Mengxi Jia",
"Bang Zhang",
"Zhaoxin Fan",
"Weiming Zhang"
] |
Poster
|
Removing unwanted concepts from large-scale text-to-image (T2I) diffusion models while maintaining their overall generative quality remains an open challenge. This difficulty is especially pronounced in emerging paradigms, such as Stable Diffusion (SD) v3 and Flux, which incorporate flow matching and transformer-based architectures. These advancements limit the transferability of existing concept-erasure techniques that were originally designed for the previous T2I paradigm (e.g., SD v1.4). In this work, we introduce EraseAnything, the first method specifically developed to address concept erasure within the latest flow-based T2I framework. We formulate concept erasure as a bi-level optimization problem, employing LoRA-based parameter tuning and an attention map regularizer to selectively suppress undesirable activations. Furthermore, we propose a self-contrastive learning strategy to ensure that removing unwanted concepts does not inadvertently harm performance on unrelated ones. Experimental results demonstrate that EraseAnything successfully fills the research gap left by earlier methods in this new T2I paradigm, achieving state-of-the-art performance across a wide range of concept erasure tasks.
|
Concept Erasing, Text2Image, Safety, Unlearning
| null | 297
|
2412.20413
|
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] |
https://github.com/tomguluson92/eraseanything
|
2,998
|
Exploring Vision Semantic Prompt for Efficient Point Cloud Understanding
|
https://openreview.net/forum?id=0gX9f5xTrD
|
[
"Yixin Zha",
"Chuxin Wang",
"Wenfei Yang",
"Tianzhu Zhang",
"Feng Wu"
] |
Poster
|
A series of pre-trained models have demonstrated promising results in point cloud understanding tasks and are widely applied to downstream tasks through fine-tuning. However, full fine-tuning leads to the forgetting of pretrained knowledge and substantial storage costs on edge devices. To address these issues, Parameter-Efficient Transfer Learning (PETL) methods have been proposed. According to our analysis, we find that existing 3D PETL methods cannot adequately align with semantic relationships of features required by downstream tasks, resulting in suboptimal performance. To ensure parameter efficiency while introducing rich semantic cues, we propose a novel fine-tuning paradigm for 3D pre-trained models. We utilize frozen 2D pre-trained models to provide vision semantic prompts and design a new Hybrid Attention Adapter to efficiently fuse 2D semantic cues into 3D representations with minimal trainable parameters(1.8M). Extensive experiments conducted on datasets including ScanObjectNN, ModelNet40, and ShapeNetPart demonstrate the effectiveness of our proposed paradigm. In particular, our method achieves 95.6% accuracy on ModelNet40 and attains 90.09% performance on the most challenging classification split ScanObjectNN(PB-T50-RS).
|
point cloud understanding; point cloud analysis; 3D computer vision
|
We propose a new paradigm that, for the first time, leverages 2D semantics as prompts to improve the generalization of pretrained 3D models with minimal trainable parameters.
| 278
| null |
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] | null |
2,999
|
Trust-Region Twisted Policy Improvement
|
https://openreview.net/forum?id=N0jZKugEuS
|
[
"Joery A. de Vries",
"Jinke He",
"Yaniv Oren",
"Matthijs T. J. Spaan"
] |
Poster
|
Monte-Carlo tree search (MCTS) has driven many recent breakthroughs in deep reinforcement learning (RL).
However, scaling MCTS to parallel compute has proven challenging in practice which has motivated alternative planners like sequential Monte-Carlo (SMC).
Many of these SMC methods adopt particle filters for smoothing through a reformulation of RL as a policy inference problem.
Yet, persisting design choices of these particle filters often conflict with the aim of online planning in RL, which is to obtain a policy improvement at the start of planning.
Drawing inspiration from MCTS, we tailor SMC planners specifically to RL by improving data generation within the planner through constrained action sampling and explicit terminal state handling, as well as improving policy and value target estimation.
This leads to our *Trust-Region Twisted* SMC (TRT-SMC), which shows improved runtime and sample-efficiency over baseline MCTS and SMC methods in both discrete and continuous domains.
|
Reinforcement Learning; Sequential Monte-Carlo; Monte-Carlo Tree Search; planning; model-based; policy improvement
|
We tailor the design choices within the particle filter planner for its specific use in reinforcement learning inspired by approaches from related Monte-Carlo tree search literature.
| 274
|
2504.06048
|
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] |
https://github.com/joeryjoery/trtpi
|
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