paper_id uint32 0 3.26k | title stringlengths 15 150 | paper_url stringlengths 42 42 | authors listlengths 1 21 | type stringclasses 3
values | abstract stringlengths 393 2.58k | keywords stringlengths 5 409 | TL;DR stringlengths 7 250 ⌀ | submission_number int64 1 16.4k | arxiv_id stringlengths 10 10 ⌀ | embedding listlengths 768 768 | github stringlengths 26 123 ⌀ |
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3,200 | CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World Web Application Vulnerabilities | https://openreview.net/forum?id=3pk0p4NGmQ | [
"Yuxuan Zhu",
"Antony Kellermann",
"Dylan Bowman",
"Philip Li",
"Akul Gupta",
"Adarsh Danda",
"Richard Fang",
"Conner Jensen",
"Eric Ihli",
"Jason Benn",
"Jet Geronimo",
"Avi Dhir",
"Sudhit Rao",
"Kaicheng Yu",
"Twm Stone",
"Daniel Kang"
] | Spotlight | Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, exis... | benchmark, cybersecurity, llm, agent | We introduce a cybersecurity benchmark for evaluating the capability of AI agents in exploiting real-world vulnerabilities of web applications. | 5,058 | null | [
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3,201 | LOCATE 3D: Real-World Object Localization via Self-Supervised Learning in 3D | https://openreview.net/forum?id=FKi6yjXwCN | [
"Paul McVay",
"Sergio Arnaud",
"Ada Martin",
"Arjun Majumdar",
"Krishna Murthy Jatavallabhula",
"Phillip Thomas",
"Ruslan Partsey",
"Daniel Dugas",
"Abha Gejji",
"Alexander Sax",
"Vincent-Pierre Berges",
"Mikael Henaff",
"Ayush Jain",
"Ang Cao",
"Ishita Prasad",
"Mrinal Kalakrishnan",
... | Spotlight | We present LOCATE 3D, a model for localizing objects in 3D scenes from referring expressions like "the small coffee table between the sofa and the lamp." LOCATE 3D sets a new state-of-the-art on standard referential grounding benchmarks and showcases robust generalization capabilities. Notably, LOCATE 3D operates direc... | self-supervised learning, object localization, referring expressions, 3D language grounding | A model that can localize objects in 3D from textual referring expressions. | 5,047 | 2504.14151 | [
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3,202 | Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN Discriminator | https://openreview.net/forum?id=OJ6WE7F8tK | [
"Kaiwen Zheng",
"Yongxin Chen",
"Huayu Chen",
"Guande He",
"Ming-Yu Liu",
"Jun Zhu",
"Qinsheng Zhang"
] | Spotlight | While likelihood-based generative models, particularly diffusion and autoregressive models, have achieved remarkable fidelity in visual generation, the maximum likelihood estimation (MLE) objective, which minimizes the forward KL divergence, inherently suffers from a mode-covering tendency that limits the generation qu... | Diffusion Models, Visual Autoregressive Models, GAN, Generation Quality | an efficient and effective finetuning method for enhancing diffusion models and visual autoregressive models | 5,007 | 2503.01103 | [
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0.... | https://github.com/NVlabs/DDO |
3,203 | Geometric Representation Condition Improves Equivariant Molecule Generation | https://openreview.net/forum?id=79O2XccGXZ | [
"Zian Li",
"Cai Zhou",
"Xiyuan Wang",
"Xingang Peng",
"Muhan Zhang"
] | Spotlight | Recent advances in molecular generative models have demonstrated great promise for accelerating scientific discovery, particularly in drug design. However, these models often struggle to generate high-quality molecules, especially in conditional scenarios where specific molecular properties must be satisfied. In this w... | molecule generation, equivariant generative models, representation, geometric deep learning, diffusion models | We propose a two-stage, model-agnostic generative approach that effectively leverages molecule representations to improve the generation quality of molecule generative models. | 4,856 | 2410.03655 | [
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0... | null |
3,204 | Graph Adaptive Autoregressive Moving Average Models | https://openreview.net/forum?id=UFlyLkvyAE | [
"Moshe Eliasof",
"Alessio Gravina",
"Andrea Ceni",
"Claudio Gallicchio",
"Davide Bacciu",
"Carola-Bibiane Schönlieb"
] | Spotlight | Graph State Space Models (SSMs) have recently been introduced to enhance Graph Neural Networks (GNNs) in modeling long-range interactions. Despite their success, existing methods either compromise on permutation equivariance or limit their focus to pairwise interactions rather than sequences. Building on the connection... | Graph Neural Networks, Auto-regressive Moving Average | We introduce GRAMA, an ARMA-based framework that preserves permutation equivariance, and adapts coefficients via selective attention for long-range propagation. Experimental results on 22 datasets demonstrate its effectiveness. | 4,819 | 2501.12732 | [
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3,205 | am-ELO: A Stable Framework for Arena-based LLM Evaluation | https://openreview.net/forum?id=EUH4VUCXay | [
"Zirui Liu",
"Jiatong Li",
"Yan Zhuang",
"Qi Liu",
"Shuanghong Shen",
"Jie Ouyang",
"Mingyue Cheng",
"Shijin Wang"
] | Spotlight | Arena-based evaluation is a fundamental yet significant evaluation paradigm for modern AI models, especially large language models (LLMs). Existing framework based on ELO rating system suffers from the inevitable instability problem due to ranking inconsistency and the lack of attention to the varying abilities of anno... | Large Language Models, Evaluation, Chatbot Arena, ELO Rating System | null | 4,626 | null | [
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3,206 | Nonparametric Teaching for Graph Property Learners | https://openreview.net/forum?id=wbvshlfyB0 | [
"Chen Zhang",
"Weixin Bu",
"Zeyi Ren",
"Zhengwu Liu",
"Yik Chung WU",
"Ngai Wong"
] | Spotlight | Inferring properties of graph-structured data, *e.g.*, the solubility of molecules, essentially involves learning the implicit mapping from graphs to their properties. This learning process is often costly for graph property learners like Graph Convolutional Networks (GCNs). To address this, we propose a paradigm calle... | Nonparametric Teaching, Graph Property Learning, Functional Gradient Descent | null | 4,554 | 2505.14170 | [
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0.0... | https://github.com/chen2hang/GraNT_NonparametricTeaching |
3,207 | Discovering a Zero (Zero-Vector Class of Machine Learning) | https://openreview.net/forum?id=u3n5wuRGTa | [
"Harikrishna Metta",
"Venkatesh Babu Radhakrishnan"
] | Spotlight | In Machine learning, separating data into classes is a very fundamental problem. A mathematical framework around the classes is presented in this work to deepen the understanding of classes. The classes are defined as vectors in a Vector Space, where addition corresponds to the union of classes, and scalar multiplicati... | Metta, Metta-Class, Metta Class, Machine Learning, ICML, Class Vector, Class Tensor Equation, Class Integration, Repository of Classes, Continual Learning, Class Addition, Class Subtraction, Class Invert, Zero Vector Class, Set operations on Classes, Boolean operation on Classes, Unary classification, Manifold learning... | The classes are defined as vectors in a Vector Space, where Addition corresponds to the union of classes, and scalar multiplication resembles set complement of classes. Zero-Vector in that vector space has many useful applications. | 4,514 | null | [
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0... | https://github.com/hm-4/Metta-Class |
3,208 | Aligning with Logic: Measuring, Evaluating and Improving Logical Preference Consistency in Large Language Models | https://openreview.net/forum?id=V61nluxFlR | [
"Yinhong Liu",
"Zhijiang Guo",
"Tianya Liang",
"Ehsan Shareghi",
"Ivan Vulić",
"Nigel Collier"
] | Spotlight | Large Language Models (LLMs) are expected to be predictable and trustworthy to support reliable decision-making systems. Yet current LLMs often show inconsistencies in their judgments. In this work, we examine \textit{logical preference consistency} as a foundational requirement for building more dependable LLM systems... | LLMs, logical consistency, order consistency, transitivity | We quantify, evaluate and improve the logical preference consistency of LLMs' judgements | 4,461 | 2410.02205 | [
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3,209 | Better to Teach than to Give: Domain Generalized Semantic Segmentation via Agent Queries with Diffusion Model Guidance | https://openreview.net/forum?id=jvP1wbD0xh | [
"Fan Li",
"Xuan Wang",
"Min Qi",
"Zhaoxiang Zhang",
"yuelei xu"
] | Spotlight | Domain Generalized Semantic Segmentation (DGSS) trains a model on a labeled source domain to generalize to unseen target domains with consistent contextual distribution and varying visual appearance.
Most existing methods rely on domain randomization or data generation but struggle to capture the underlying scene distr... | semantic segmentation, domain generalization, diffusion model | null | 4,280 | null | [
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... | null |
3,210 | P(all-atom) Is Unlocking New Path For Protein Design | https://openreview.net/forum?id=yXRixu0ONY | [
"Wei Qu",
"Jiawei Guan",
"Rui Ma",
"kezhai",
"Weikun.Wu",
"Haobo Wang"
] | Spotlight | We introduce Pallatom, an innovative protein generation model capable of producing protein structures with all-atom coordinates. Pallatom directly learns and models the joint distribution $P(\textit{structure}, \textit{seq})$ by focusing on $P(\textit{all-atom})$, effectively addressing the interdependence between sequ... | Proteins, Generative models, Co-design, All-atom | A state-of- the-art all-atom protein generative model. | 4,192 | null | [
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0.... | https://github.com/levinthal/Pallatom |
3,211 | The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes | https://openreview.net/forum?id=I4jNAbqHnM | [
"Pedro Pinto Santos",
"Alberto Sardinha",
"Francisco S. Melo"
] | Spotlight | The general-utility Markov decision processes (GUMDPs) framework generalizes the MDPs framework by considering objective functions that depend on the frequency of visitation of state-action pairs induced by a given policy. In this work, we contribute with the first analysis on the impact of the number of trials, i.e., ... | Planning, sequential decision-making, general-utility markov decision processes, convex markov decision processes | null | 4,103 | 2409.15128 | [
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3,212 | On the Benefits of Active Data Collection in Operator Learning | https://openreview.net/forum?id=hYHczNrKoX | [
"Unique Subedi",
"Ambuj Tewari"
] | Spotlight | We study active data collection strategies for operator learning when the target operator is linear and the input functions are drawn from a mean-zero stochastic process with continuous covariance kernels. With an active data collection strategy, we establish an error convergence rate in terms of the decay rate of the ... | Operator Learning, Active Learning, PDEs | We study active data collection strategies for operator learning and establish their provable advantage over passive sampling approaches. | 4,078 | 2410.19725 | [
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3,213 | Re-ranking Reasoning Context with Tree Search Makes Large Vision-Language Models Stronger | https://openreview.net/forum?id=DJcEoC9JpQ | [
"Qi Yang",
"Chenghao Zhang",
"Lubin Fan",
"Kun Ding",
"Jieping Ye",
"Shiming Xiang"
] | Spotlight | Recent advancements in Large Vision Language Models (LVLMs) have significantly improved performance in Visual Question Answering (VQA) tasks through multimodal Retrieval-Augmented Generation (RAG). However, existing methods still face challenges, such as the scarcity of knowledge with reasoning examples and erratic res... | Large Vision Language Model, Multimodal Retrieval-Augmented Generation, In-context Learning, Monte Carlo Tree Search | We propose RCTS, a multimodal RAG framework that enhances LVLMs for VQA tasks by integrating a reasoning-context-enriched knowledge base and tree-search re-ranking, achieving state-of-the-art performance. | 3,925 | 2506.07785 | [
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3,214 | Neural Collapse Beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime | https://openreview.net/forum?id=ZrhGq664om | [
"Diyuan Wu",
"Marco Mondelli"
] | Spotlight | Neural Collapse is a phenomenon where the last-layer representations of a well-trained neural network converge to a highly structured geometry. In this paper, we focus on its first (and most basic) property, known as NC1: the within-class variability vanishes.
While prior theoretical studies establish the occurrence o... | neural collapse, mean-field analysis, gradient flow, generalization error, loss landscape | We prove that NC1 (vanishing within-class variability) holds when training a class of 3-layer networks via gradient flow, due to loss landscape properties; we further show co-occurrence of NC1 and small test error for certain data distributions. | 3,886 | 2501.19104 | [
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0.01... | https://github.com/DiyuanWu/icml25_expr |
3,215 | Invariant Deep Uplift Modeling for Incentive Assignment in Online Marketing via Probability of Necessity and Sufficiency | https://openreview.net/forum?id=mruyFvKDKq | [
"Zexu Sun",
"Qiyu Han",
"Hao Yang",
"Anpeng Wu",
"Minqin Zhu",
"Dugang Liu",
"Chen Ma",
"Yunpeng Weng",
"Xing Tang",
"xiuqiang He"
] | Spotlight | In online platforms, incentives (\textit{e.g}., discounts, coupons) are used to boost user engagement and revenue. Uplift modeling methods are developed to estimate user responses from observational data, often incorporating distribution balancing to address selection bias. However, these methods are limited by in-dist... | Uplift modeling, Invariant learning, Incentives assignment, Online marketing | This paper proposes an invariant learning based uplift modeling method, which aims to solve the out-of-distribution problem in online marketing. | 3,824 | null | [
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3,216 | G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks | https://openreview.net/forum?id=LpE54NUnmO | [
"Guibin Zhang",
"Yanwei Yue",
"Xiangguo Sun",
"Guancheng Wan",
"Miao Yu",
"Junfeng Fang",
"Kun Wang",
"Tianlong Chen",
"Dawei Cheng"
] | Spotlight | Recent advancements in large language model (LLM)-based agents have demonstrated that collective intelligence can significantly surpass the capabilities of individual agents, primarily due to well-crafted inter-agent communication topologies. Despite the diverse and high-performing designs available, practitioners ofte... | Multi-agent communication, Graph machine learning, LLM-based agent | null | 3,779 | null | [
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3,217 | SAFE: Finding Sparse and Flat Minima to Improve Pruning | https://openreview.net/forum?id=10l1pGeOcK | [
"Dongyeop Lee",
"Kwanhee Lee",
"Jinseok Chung",
"Namhoon Lee"
] | Spotlight | Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.
Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat... | Pruning, Constrained optimization, Sharpness minimization | We propose SAFE, an optimization-based pruning method that improves generalization of sparse models by inducing flatness. | 3,619 | 2506.06866 | [
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-... | https://github.com/LOG-postech/safe-torch,https://github.com/LOG-postech/safe-jax |
3,218 | On the Guidance of Flow Matching | https://openreview.net/forum?id=pKaNgFzJBy | [
"Ruiqi Feng",
"Chenglei Yu",
"Wenhao Deng",
"Peiyan Hu",
"Tailin Wu"
] | Spotlight | Flow matching has shown state-of-the-art performance in various generative tasks, ranging from image generation to decision-making, where generation under energy guidance (abbreviated as guidance in the following) is pivotal. However, the guidance of flow matching is more general than and thus substantially different f... | flow matching, guided generation, generative modeling | We introduce the first framework for general flow matching guidance, from which new guidance methods are derived and many classical guidance methods are covered as special cases. | 3,557 | 2502.02150 | [
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... | https://github.com/AI4Science-WestlakeU/flow_guidance |
3,219 | TLLC: Transfer Learning-based Label Completion for Crowdsourcing | https://openreview.net/forum?id=BkdAnSKNoX | [
"Wenjun Zhang",
"Liangxiao Jiang",
"Chaoqun Li"
] | Spotlight | Label completion serves as a preprocessing approach to handling the sparse crowdsourced label matrix problem, significantly boosting the effectiveness of the downstream label aggregation. In recent advances, worker modeling has been proved to be a powerful strategy to further improve the performance of label completion... | Crowdsourcing learning, Label Completion, Worker modeling, Transfer Learning | This paper proposes a novel transfer learning-based label completion (TLLC) algorithm. | 3,322 | null | [
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0.008... | https://github.com/jiangliangxiao/TLLC |
3,220 | Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces | https://openreview.net/forum?id=U354tbTjav | [
"Henry Moss",
"Sebastian W. Ober",
"Tom Diethe"
] | Spotlight | Bayesian optimisation in the latent space of a VAE is a powerful framework for optimisation tasks over complex structured domains, such as the space of valid molecules. However, existing approaches tightly couple the surrogate and generative models, which can lead to suboptimal performance when the latent space is not ... | Bayesian Optimisation | Don't do Bayesian optimisation in the latent space of a VAE .... | 3,247 | 2507.03910 | [
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0.009... | null |
3,221 | New Bounds for Sparse Variational Gaussian Processes | https://openreview.net/forum?id=Ppcf30NGL0 | [
"Michalis Titsias"
] | Spotlight | Sparse variational Gaussian processes (GPs) construct tractable posterior approximations to GP models. At the core of these methods is the assumption that the true posterior distribution over training function values ${\bf f}$ and inducing variables ${\bf u}$ is approximated by a variational distribution that incorpor... | Sparse variational Gaussian process, new collapsed bound | It presents new collapsed and uncollapsed bounds for sparse variational Gaussian processes using inducing points. | 3,219 | 2502.08730 | [
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3,222 | An Error Analysis of Flow Matching for Deep Generative Modeling | https://openreview.net/forum?id=vES22INUKm | [
"Zhengyu Zhou",
"Weiwei Liu"
] | Spotlight | Continuous Normalizing Flows (CNFs) have proven to be a highly efficient technique for generative modeling of complex data since the introduction of Flow Matching (FM). The core of FM is to learn the constructed velocity fields of CNFs through deep least squares regression. Despite its empirical effectiveness, theoreti... | Statistical Learning Theory | null | 3,098 | null | [
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3,223 | Efficient First-Order Optimization on the Pareto Set for Multi-Objective Learning under Preference Guidance | https://openreview.net/forum?id=PUzNwYmb3l | [
"Lisha Chen",
"Quan Xiao",
"Ellen Hidemi Fukuda",
"Xinyi Chen",
"Kun Yuan",
"Tianyi Chen"
] | Spotlight | Multi-objective learning under user-specified preference is common in real-world problems such as multi-lingual speech recognition under fairness. In this work, we frame such a problem as a semivectorial bilevel optimization problem, whose goal is to optimize a pre-defined preference function, subject to the constraint... | multi-objective optimization, optimization on the Pareto set, semivectorial bilevel optimization | We cast the preference-guided multi-objective learning problem as optimization on the Pareto set, and propose a first-order penalty approach to solve it. | 3,045 | 2504.02854 | [
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3,224 | Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios | https://openreview.net/forum?id=iFOXz5H2gB | [
"Xihong Yang",
"Siwei Wang",
"Fangdi Wang",
"Jiaqi Jin",
"Suyuan Liu",
"Yue Liu",
"En Zhu",
"Xinwang Liu",
"Yueming Jin"
] | Spotlight | Leveraging the powerful representation learning capabilities, deep multi-view clustering methods have demonstrated reliable performance by effectively integrating multi-source information from diverse views in recent years. Most existing methods rely on the assumption of clean views. However, noise is pervasive in real... | Multi-view Clustering; Contrastive Learning; Noisy Scenarios | null | 2,848 | 2505.21387 | [
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... | null |
3,225 | Improving Zero-Shot Adversarial Robustness in Vision-Language Models by Closed-form Alignment of Adversarial Path Simplices | https://openreview.net/forum?id=WR0ahlhOoy | [
"Junhao Dong",
"Piotr Koniusz",
"Yifei Zhang",
"Hao Zhu",
"Weiming Liu",
"Xinghua Qu",
"Yew-Soon Ong"
] | Spotlight | Vision-Language Models (VLMs) such as CLIP excel at zero-shot classification due to large-scale pre-training but are vulnerable to adversarial examples. Adversarial fine-tuning robustifies zero-shot models by aligning prediction scores of individual adversaries with their clean counterparts, which typically overlooks i... | Vision-Language Models, Adversarial Examples, Zero-Shot Classification | We robustify VLMs by aligning entire adversarial simplices rather than individual adversarial samples with classifier scores of clean samples. | 2,781 | null | [
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3,226 | Graph Diffusion for Robust Multi-Agent Coordination | https://openreview.net/forum?id=T5IZ32ImAB | [
"Xianghua Zeng",
"Hang Su",
"Zhengyi Wang",
"Zhiyuan LIN"
] | Spotlight | Offline multi-agent reinforcement learning (MARL) struggles to estimate out-of-distribution states and actions due to the absence of real-time environmental feedback. While diffusion models show promise in addressing these challenges, their application primarily focuses on independently diffusing the historical traject... | multi-agent coordination, offline reinforcement learning, diffusion models | null | 2,772 | null | [
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3,227 | Weakly-Supervised Contrastive Learning for Imprecise Class Labels | https://openreview.net/forum?id=Y19ngWhN0b | [
"Zi-Hao Zhou",
"Jun-Jie Wang",
"Tong Wei",
"Min-Ling Zhang"
] | Spotlight | Contrastive learning has achieved remarkable success in learning effective representations, with supervised contrastive learning often outperforming self-supervised approaches. However, in real-world scenarios, data annotations are often ambiguous or inaccurate, meaning that class labels may not reliably indicate wheth... | Weakly-supervised learning, Contrastive learning, Noisy label learning, Partial label learning | null | 2,740 | 2505.22028 | [
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0.01751432... | https://github.com/Speechless-10308/WSC |
3,228 | Robust Automatic Modulation Classification with Fuzzy Regularization | https://openreview.net/forum?id=DDIGCk25BO | [
"Xinyan Liang",
"Ruijie Sang",
"Yuhua Qian",
"Qian Guo",
"Feijiang Li",
"Liang Du"
] | Spotlight | Automatic Modulation Classification (AMC) serves as a foundational pillar for cognitive radio systems, enabling critical functionalities including dynamic spectrum allocation, non-cooperative signal surveillance, and adaptive waveform optimization. However, practical deployment of AMC faces a fundamental challenge: pre... | Robustness, Fuzzy Regularization, Automatic Modulation Classification | null | 2,696 | null | [
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0... | https://github.com/ruijiesang/FR-AMC |
3,229 | Language Models May Verbatim Complete Text They Were Not Explicitly Trained On | https://openreview.net/forum?id=bLcXkIasck | [
"Ken Liu",
"Christopher A. Choquette-Choo",
"Matthew Jagielski",
"Peter Kairouz",
"Sanmi Koyejo",
"Percy Liang",
"Nicolas Papernot"
] | Spotlight | An important question today is whether a given text was used to train a large language model (LLM). A completion test is often employed: check if the LLM completes a sufficiently complex text. This, however, requires a ground-truth definition of membership; most commonly, it is defined as a member based on the n-gram o... | Training data membership, data completion, data reconstruction, membership inference, unlearning, privacy, training set inclusion, copyright | Under $n$-gram definitions of train-set inclusion, LLMs can complete “unseen” texts—both after data deletion and adding “gibberish” data. Our results impact unlearning, membership inference & data transparency. | 2,670 | 2503.17514 | [
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3,230 | Taming Knowledge Conflicts in Language Models | https://openreview.net/forum?id=0cEZyhHEks | [
"Gaotang Li",
"Yuzhong Chen",
"Hanghang Tong"
] | Spotlight | Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge.
Previous works attribute this conflict to the interplay between "memory heads" and "context heads", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond thi... | Knowledge Conflict, Mechanistic Interpretability, Science of Large Language Models | null | 2,610 | 2503.10996 | [
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3,231 | UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control | https://openreview.net/forum?id=uqCfoVXb67 | [
"Kaizhen Zhu",
"Mokai Pan",
"Yuexin Ma",
"Yanwei Fu",
"Jingyi Yu",
"Jingya Wang",
"Ye Shi"
] | Spotlight | Recent advances in diffusion bridge models leverage Doob’s $h$-transform to establish fixed endpoints between distributions, demonstrating promising results in image translation and restoration tasks. However, these approaches frequently produce blurred or excessively smoothed image details and lack a comprehensive the... | Diffusion bridge, Doob's h-transform, Stochastic optimal control | We present UniDB, a unified diffusion bridge framework using stochastic optimal control, significantly improving detail preservation and image quality in generative tasks with minimal code modifications. | 2,519 | 2502.05749 | [
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3,232 | K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting | https://openreview.net/forum?id=71Mm8GDGYd | [
"Xingjian Wu",
"Xiangfei Qiu",
"Hongfan Gao",
"Jilin Hu",
"Bin Yang",
"Chenjuan Guo"
] | Spotlight | Probabilistic Time Series Forecasting (PTSF) plays a crucial role in decision-making across various fields, including economics, energy, and transportation. Most existing methods excell at short-term forecasting, while overlooking the hurdles of Long-term Probabilistic Time Series Forecasting (LPTSF). As the forecast h... | Time Series Probabilistic Forecasting | null | 2,349 | 2505.23017 | [
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0.... | https://github.com/decisionintelligence/K2VAE |
3,233 | Doubly Robust Conformalized Survival Analysis with Right-Censored Data | https://openreview.net/forum?id=2PWn1LtCwP | [
"Matteo Sesia",
"Vladimir Svetnik"
] | Spotlight | We present a conformal inference method for constructing lower prediction bounds for survival times from right-censored data, extending recent approaches designed for more restrictive type-I censoring scenarios. The proposed method imputes unobserved censoring times using a machine learning model, and then analyzes the... | Conformal inference, Survival analysis, Uncertainty Estimation | This paper presents a conformal inference method for constructing lower prediction bounds for survival times from right-censored data. | 2,249 | 2412.09729 | [
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0.00... | https://github.com/msesia/conformal_survival |
3,234 | HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation | https://openreview.net/forum?id=WbP2OwMULq | [
"Tianwei Lin",
"Wenqiao Zhang",
"SIJING LI",
"Yuqian Yuan",
"Binhe Yu",
"Haoyuan Li",
"Wanggui He",
"Hao Jiang",
"Mengze Li",
"Song xiaohui",
"Siliang Tang",
"Jun Xiao",
"Hui Lin",
"Yueting Zhuang",
"Beng Chin Ooi"
] | Spotlight | We present **HealthGPT**, a powerful Medical Large Vision-Language Model (Med-LVLM) that integrates medical visual comprehension and generation capabilities within a unified autoregressive paradigm. Our bootstrapping philosophy is to progressively adapt heterogeneous comprehension and generation knowledge to pre-traine... | Medical Large Vision-Language Models; Multi-Modal Comprehension and Generation | null | 2,225 | 2502.09838 | [
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0.00... | https://github.com/DCDmllm/HealthGPT |
3,235 | TimeBase: The Power of Minimalism in Efficient Long-term Time Series Forecasting | https://openreview.net/forum?id=GhTdNOMfOD | [
"Qihe Huang",
"Zhengyang Zhou",
"Kuo Yang",
"Zhongchao Yi",
"Xu Wang",
"Yang Wang"
] | Spotlight | Long-term time series forecasting (LTSF) has traditionally relied on large parameters to capture extended temporal dependencies, resulting in substantial computational costs and inefficiencies in both memory usage and processing time.
However, time series data, unlike high-dimensional images or text, often exhibit te... | Time series forecasting | null | 2,176 | null | [
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0.0220803... | null |
3,236 | Catch Your Emotion: Sharpening Emotion Perception in Multimodal Large Language Models | https://openreview.net/forum?id=IYOksPHJKT | [
"Yiyang Fang",
"Jian Liang",
"Wenke Huang",
"He Li",
"Kehua Su",
"Mang Ye"
] | Spotlight | Multimodal large language models (MLLMs) have achieved impressive progress in tasks such as visual question answering and visual understanding, but they still face significant challenges in emotional reasoning. Current methods to enhance emotional understanding typically rely on fine-tuning or manual annotations, which... | Multimodal Large Language Models, Emotion Recognition, Training-Free | We propose SEPM to enhance MLLMs' emotion recognition by refining classification through a two-stage inference and reducing visual redundancy, offering a scalable, resource-efficient solution. | 2,164 | null | [
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... | https://github.com/fuyyyyy/SEPM |
3,237 | Layer-wise Alignment: Examining Safety Alignment Across Image Encoder Layers in Vision Language Models | https://openreview.net/forum?id=F1ff8zcjPp | [
"Saketh Bachu",
"Erfan Shayegani",
"Rohit Lal",
"Trishna Chakraborty",
"Arindam Dutta",
"Chengyu Song",
"Yue Dong",
"Nael B. Abu-Ghazaleh",
"Amit Roy-Chowdhury"
] | Spotlight | Vision-language models (VLMs) have improved significantly in their capabilities, but their complex architecture makes their safety alignment challenging. In this paper, we reveal an uneven distribution of harmful information across the intermediate layers of the image encoder and show that skipping a certain set of lay... | Vision Language Models, Safety Alignment, Reinforcement Learning from Human Feedback (RLHF) | We reveal an image encoder early exit based vulnerability in VLMs and propose layer-wise RLHF to alleviate it. | 2,098 | 2411.04291 | [
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... | null |
3,238 | Training Dynamics of In-Context Learning in Linear Attention | https://openreview.net/forum?id=aFNq67ilos | [
"Yedi Zhang",
"Aaditya K Singh",
"Peter E. Latham",
"Andrew M Saxe"
] | Spotlight | While attention-based models have demonstrated the remarkable ability of in-context learning (ICL), the theoretical understanding of how these models acquired this ability through gradient descent training is still preliminary. Towards answering this question, we study the gradient descent dynamics of multi-head linear... | learning dynamics, in-context learning, linear attention | We theoretically characterize how in-context learning abilities evolve during gradient descent training of linear attention, revealing abrupt acquisition or progressive improvements depending on how the key and query are parametrized. | 1,939 | 2501.16265 | [
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-0.0... | https://github.com/yedizhang/linattn-icl |
3,239 | When and How Does CLIP Enable Domain and Compositional Generalization? | https://openreview.net/forum?id=Lktwi30g63 | [
"Elias Kempf",
"Simon Schrodi",
"Max Argus",
"Thomas Brox"
] | Spotlight | The remarkable generalization performance of contrastive vision-language models like CLIP is often attributed to the diversity of their training distributions. However, key questions remain unanswered: Can CLIP generalize to an entirely unseen domain when trained on a diverse mixture of domains (domain generalization)... | CLIP, Compositional Generalization, Domain Generalization, Out-of-Distribution Robustness, OOD generalization | We studied CLIP's domain and compositional generalization via systematic data-centric experiments and mechanistic analyses, revealing that domain diversity, sufficiently shared intermediate features and circuitry are crucial for generalization. | 1,549 | 2502.09507 | [
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0.003... | https://github.com/lmb-freiburg/understanding-clip-ood |
3,240 | Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain | https://openreview.net/forum?id=Bm706VlAtU | [
"Gaozheng Pei",
"Ke Ma",
"Yingfei Sun",
"Qianqian Xu",
"Qingming Huang"
] | Spotlight | The diffusion-based adversarial purification methods attempt to drown adversarial perturbations into a part of isotropic noise through the forward process, and then recover the clean images through the reverse process. Due to the lack of distribution information about adversarial perturbations in the pixel domain, it i... | Adversarial Purification | Adversarial Purification from Perspective of Frequency Domain | 1,502 | 2505.01267 | [
-0.0038476360496133566,
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-0.0485905632376670... | https://github.com/GaozhengPei/FreqPure |
3,241 | Leveraging Diffusion Model as Pseudo-Anomalous Graph Generator for Graph-Level Anomaly Detection | https://openreview.net/forum?id=Zm2M92TZyO | [
"Jinyu Cai",
"Yunhe Zhang",
"Fusheng Liu",
"See-Kiong Ng"
] | Spotlight | A fundamental challenge in graph-level anomaly detection (GLAD) is the scarcity of anomalous graph data, as the training dataset typically contains only normal graphs or very few anomalies. This imbalance hinders the development of robust detection models. In this paper, we propose **A**nomalous **G**raph **Diff**usion... | Anomaly Detection, Diffusion Model, Graph Neural Network | null | 1,490 | null | [
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3,242 | An Analysis for Reasoning Bias of Language Models with Small Initialization | https://openreview.net/forum?id=4HQaMUYWAT | [
"Junjie Yao",
"Zhongwang Zhang",
"Zhi-Qin John Xu"
] | Spotlight | Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initializ... | initialization scale, reasoning bias, language model, embedding space, training dynamics | null | 1,338 | 2502.04375 | [
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3,243 | Instance Correlation Graph-based Naive Bayes | https://openreview.net/forum?id=hwTKGdM4TK | [
"Chengyuan Li",
"Liangxiao Jiang",
"Wenjun Zhang",
"Liangjun Yu",
"Huan Zhang"
] | Spotlight | Due to its simplicity, effectiveness and robustness, naive Bayes (NB) has continued to be one of the top 10 data mining algorithms. To improve its performance, a large number of improved algorithms have been proposed in the last few decades. However, in addition to Gaussian naive Bayes (GNB), there is little work on nu... | Naive Bayes, Numerical attribute, Instance correlation graph, Variational graph auto-encoder | A novel instance correlation graph-based naive Bayes (ICGNB) algorithm is proposed. | 1,002 | null | [
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0.... | https://github.com/jiangliangxiao/ICGNB |
3,244 | Trusted Multi-View Classification with Expert Knowledge Constraints | https://openreview.net/forum?id=U64wEbM7NB | [
"Xinyan Liang",
"Shijie Wang",
"Yuhua Qian",
"Qian Guo",
"Liang Du",
"Bingbing Jiang",
"Tingjin Luo",
"Feijiang Li"
] | Spotlight | Multi-view classification (MVC) based on the Dempster-Shafer theory has gained significant recognition for its reliability in safety-critical applications. However, existing methods predominantly focus on providing confidence levels for decision outcomes without explaining the reasoning behind these decisions. Moreover... | multi-view classification, trusted multi-view classification, trusted fusion, distribution-aware subjective opinion | null | 850 | null | [
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0.03820854797959328,
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0.0319... | https://github.com/jie019/TMCEK_ICML2025 |
3,245 | Discrepancy Minimization in Input-Sparsity Time | https://openreview.net/forum?id=TmJvacopmV | [
"Yichuan Deng",
"Xiaoyu Li",
"Zhao Song",
"OMRI WEINSTEIN"
] | Spotlight | A recent work by [Larsen, SODA 2023] introduced a faster combinatorial alternative to Bansal's SDP algorithm for finding a coloring $x \in \\{-1, 1\\}^n$ that approximately minimizes the discrepancy $\mathrm{disc}(A, x) := \\| A x \\|_{\infty}$ of a real-valued $m \times n$ matrix $A$. Larsen's algorithm runs in $\wide... | combinatorial optimization, algorithmic discrepancy theory, sketching, input-sparsity time | We give the algorithm for discrepancy minimization which runs in input-sparsity time. | 816 | 2210.12468 | [
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3,246 | Sharp Generalization for Nonparametric Regression by Over-Parameterized Neural Networks: A Distribution-Free Analysis in Spherical Covariate | https://openreview.net/forum?id=fPOkujQBVb | [
"Yingzhen Yang"
] | Spotlight | Sharp generalization bound for neural networks trained by gradient descent (GD) is of central interest in statistical learning theory and deep learning. In this paper, we consider nonparametric regression
by an over-parameterized two-layer NN trained by GD. We show that, if the neural network is trained by GD with earl... | Nonparametric Regression, Over-Parameterized Neural Network, Gradient Descent, Minimax Optimal Rate | We show that an over-parameterized two-layer neural network trained by gradient descent (GD) exhibits minimax optimal convergence rates for nonparametric regression, and our results are distribution-free in spherical covariate. | 572 | null | [
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3,247 | Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss | https://openreview.net/forum?id=S2K5MyRjrL | [
"Bo-Han Lai",
"Pin-Han Huang",
"Bo-Han Kung",
"Shang-Tse Chen"
] | Spotlight | Lipschitz neural networks are well-known for providing certified robustness in deep learning. In this paper, we present a novel, efficient Block Reflector Orthogonal (BRO) layer that enhances the capability of orthogonal layers on constructing more expressive Lipschitz neural architectures. In addition, by theoreticall... | Certified robustness, Adversarial | We propose a new orthogonal convolution and a novel loss function to enhance certified robustness. | 559 | 2505.15174 | [
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0... | https://github.com/ntuaislab/BRONet |
3,248 | Relational Invariant Learning for Robust Solvation Free Energy Prediction | https://openreview.net/forum?id=xVBfdltHST | [
"yeyunchen"
] | Spotlight | Predicting the solvation free energy of molecules using graph neural networks holds significant potential for advancing drug discovery and the design of novel materials. While previous methods have demonstrated success on independent and identically distributed (IID) datasets, their performance in out-of-distribution (... | Molecule relational learning, graph neural network, out of distribution generalization | null | 496 | null | [
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3,249 | Adaptive Multi-prompt Contrastive Network for Few-shot Out-of-distribution Detection | https://openreview.net/forum?id=GoGuB1yFko | [
"Xiang Fang",
"Arvind Easwaran",
"Blaise Genest"
] | Spotlight | Out-of-distribution (OOD) detection attempts to distinguish outlier samples to prevent models trained on the in-distribution (ID) dataset from producing unavailable outputs. Most OOD detection methods require many ID samples for training, which seriously limits their real-world applications. To this end, we target a ch... | Adaptive Multi-prompt Contrastive Network | null | 484 | 2506.17633 | [
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0.01389537937939167,
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0.016079582273960114,
-0.06372295320034027,
-0.01... | null |
3,250 | FlashTP: Fused, Sparsity-Aware Tensor Product for Machine Learning Interatomic Potentials | https://openreview.net/forum?id=wiQe95BPaB | [
"Seung Yul Lee",
"Hojoon Kim",
"Yutack Park",
"Dawoon Jeong",
"Seungwu Han",
"Yeonhong Park",
"Jae W. Lee"
] | Spotlight | Machine Learning Interatomic Potentials (MLIPs) enable efficient molecular dynamics (MD) simulations with high accuracy. While equivariant MLIPs achieve state-of-the-art accuracy, they face significant computational bottlenecks centered around their Tensor-Product layer, which account for up to 75\% of training time an... | Equivariant neural networks, Tensor Product, Software libraries, Efficiency, Machine-learned interatomic potential (MLIP), Machine Learning Force Fields (MLFF) | FlashTP accelerates equivariant MLIPs by optimizing Tensor-Product operations, achieving up to speedup and significantly reducing memory footprint. | 450 | null | [
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0.018473457545042038,
-0.0020686518400907516,
-0.05478601157665253,
... | https://github.com/SNU-ARC/flashTP |
3,251 | Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems | https://openreview.net/forum?id=GazlTYxZss | [
"Shaokun Zhang",
"Ming Yin",
"Jieyu Zhang",
"Jiale Liu",
"Zhiguang Han",
"Jingyang Zhang",
"Beibin Li",
"Chi Wang",
"Huazheng Wang",
"Yiran Chen",
"Qingyun Wu"
] | Spotlight | Failure attribution in LLM multi-agent systems—identifying the agent and step responsible for task failures—provides crucial clues for systems debugging but remains underexplored and labor-intensive.
In this paper, we propose and formulate a new research area: automated failure attribution for LLM multi-agent systems.... | failure attribution, multi-agent systems. | null | 425 | 2505.00212 | [
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0.03581684082746506,
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... | https://github.com/mingyin1/Agents_Failure_Attribution |
3,252 | A Closer Look at Multimodal Representation Collapse | https://openreview.net/forum?id=Vf9f7eNX6T | [
"Abhra Chaudhuri",
"Anjan Dutta",
"Tu Bui",
"Serban Georgescu"
] | Spotlight | We aim to develop a fundamental understanding of modality collapse, a recently observed empirical phenomenon wherein models trained for multimodal fusion tend to rely only on a subset of the modalities, ignoring the rest. We show that modality collapse happens when noisy features from one modality are entangled, via a ... | Multimodal learning, modality collapse | Modality collapse happens as a result of cross-modal polysemantic entanglements arising out of rank bottlenecks in deep multimodal models, and can thus be remedied by freeing up such bottlenecks. | 421 | 2505.22483 | [
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0.0... | null |
3,253 | Geometric Hyena Networks for Large-scale Equivariant Learning | https://openreview.net/forum?id=jJRkkPr474 | [
"Artem Moskalev",
"Mangal Prakash",
"Junjie Xu",
"Tianyu Cui",
"Rui Liao",
"Tommaso Mansi"
] | Spotlight | Processing global geometric context while preserving equivariance is crucial when modeling biological, chemical, and physical systems. Yet, this is challenging due to the computational demands of equivariance and global context at scale. Standard methods such as equivariant self-attention suffer from quadratic complexi... | equivariance, global context, long convolution, scalability, mechanistic interpretability, architecrture | Geometric Hyena Networks is the first equivariant long-convolutional model that efficiently captures global geometric context at sub-quadratic complexity | 339 | 2505.22560 | [
0.011044753715395927,
0.01563769206404686,
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0.000184315606020391,
0.01413695141673088,
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0.015031042508780956,
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0.026... | null |
3,254 | Covered Forest: Fine-grained generalization analysis of graph neural networks | https://openreview.net/forum?id=xvLVYrYQ8a | [
"Antonis Vasileiou",
"Ben Finkelshtein",
"Floris Geerts",
"Ron Levie",
"Christopher Morris"
] | Spotlight | The expressive power of message-passing graph neural networks (MPNNs) is reasonably well understood, primarily through combinatorial techniques from graph isomorphism testing. However, MPNNs' generalization abilities---making meaningful predictions beyond the training set---remain less explored. Current generalization ... | MPNNs, generalization, bounds, theory, Weisfeiler, Leman, Lehman | We provide tighter generalization bounds for MPNNs by considering the pseudometric geometry of MPNNs' feature space | 252 | 2412.07106 | [
-0.021143797785043716,
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0.0104669239372015,
-0.01618114858865738,
-0.07563044875860214,
-0.002101... | https://github.com/benfinkelshtein/CoveredForests |
3,255 | Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning | https://openreview.net/forum?id=OpineZj5bj | [
"Shuai Yi",
"Yixiong Zou",
"Yuhua Li",
"Ruixuan Li"
] | Spotlight | Vision Transformer (ViT) has achieved remarkable success due to its large-scale pretraining on general domains, but it still faces challenges when applying it to downstream distant domains that have only scarce training data, which gives rise to the Cross-Domain Few-Shot Learning (CDFSL) task. Inspired by Self-Attentio... | Cross-Domain Few-Shot Learning | We find a phenomenon that disrupting image patches' continuity (e.g., shuffle patches) affects differently on source and target domains. We delve into it for an interpretation and propose a method based on it for CDFSL. | 247 | 2506.03110 | [
0.013432389125227928,
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0.020158257335424423,
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... | https://github.com/shuaiyi308/ReCIT |
3,256 | On the Tension between Byzantine Robustness and No-Attack Accuracy in Distributed Learning | https://openreview.net/forum?id=zU4VCPHYRC | [
"Yi-Rui Yang",
"Chang-Wei Shi",
"Wu-Jun Li"
] | Spotlight | Byzantine-robust distributed learning (BRDL), which refers to distributed learning that can work with potential faulty or malicious workers (also known as Byzantine workers), has recently attracted much research attention. Robust aggregators are widely used in existing BRDL methods to obtain robustness against Byzantin... | distributed machine learning, Byzantine robustness, robust aggregation | null | 121 | null | [
-0.02111426554620266,
-0.0030951371882110834,
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0.029228923842310905,
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0.04632173478603363,
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0.0015276939375326037,
-0.007225915789604187,
-0.06459060311317444,
... | null |
3,257 | Determining Layer-wise Sparsity for Large Language Models Through a Theoretical Perspective | https://openreview.net/forum?id=otNB7BzsiR | [
"Weizhong Huang",
"Yuxin Zhang",
"Xiawu Zheng",
"Fei Chao",
"Rongrong Ji"
] | Spotlight | In this paper, we address the challenge of determining the layer-wise sparsity rates of large language models (LLMs) through a theoretical perspective. Specifically, we identify a critical issue of **"reconstruction error explosion"** in existing LLMs sparsification methods. This refers to the cumulative effect of reco... | Large language models, Network Sparsity, Layerwise sparsity | We derive the layer-wise sparsity rate of LLMs through a theoretical perspective, which significantly enhances the performance of sparse LLMs. | 58 | 2502.14770 | [
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0.0028990362770855427,
0.023747634142637253,
-0.03467974439263344,
0.011... | https://github.com/wzhuang-xmu/ATP |
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