paper_id uint32 0 3.7k | title stringlengths 14 154 | paper_url stringlengths 42 42 | authors listlengths 1 21 | type stringclasses 3
values | abstract stringlengths 413 2.52k | keywords stringlengths 4 397 | TL;DR stringlengths 5 250 ⌀ | submission_number int64 2 14.3k | arxiv_id stringlengths 10 10 ⌀ | embedding listlengths 768 768 |
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100 | SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning | https://openreview.net/forum?id=5U1rlpX68A | [
"Yichen Wu",
"Hongming Piao",
"Long-Kai Huang",
"Renzhen Wang",
"Wanhua Li",
"Hanspeter Pfister",
"Deyu Meng",
"Kede Ma",
"Ying Wei"
] | Oral | Continual Learning (CL) with foundation models has recently emerged as a promising paradigm to exploit abundant knowledge acquired during pre-training for tackling sequential tasks. However, existing prompt-based and Low-Rank Adaptation-based (LoRA-based) methods often require expanding a prompt/LoRA pool or retaining ... | Continual learning; Low-rank adaptation | null | 6,765 | null | [
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101 | Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching | https://openreview.net/forum?id=fV0t65OBUu | [
"Zijing Ou",
"Mingtian Zhang",
"Andi Zhang",
"Tim Z. Xiao",
"Yingzhen Li",
"David Barber"
] | Oral | The probabilistic diffusion model has become highly effective across various domains. Typically, sampling from a diffusion model involves using a denoising distribution characterized by a Gaussian with a learned mean and either fixed or learned covariances. In this paper, we leverage the recently proposed covariance mo... | Diffusion Model, Generative Model, Probalistic Modelling | We introduce Optimal Covariance Matching (OCM), a novel method that improves sampling efficiency and accuracy in diffusion models by directly regressing optimal analytic covariances. | 6,659 | 2406.10808 | [
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102 | PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration | https://openreview.net/forum?id=rFpZnn11gj | [
"Yuxuan Sun",
"Yunlong Zhang",
"Yixuan Si",
"Chenglu Zhu",
"Kai Zhang",
"Zhongyi Shui",
"Jingxiong Li",
"Xuan Gong",
"XINHENG LYU",
"Tao Lin",
"Lin Yang"
] | Oral | Vision Language Models (VLMs) like CLIP have attracted substantial attention in pathology, serving as backbones for applications such as zero-shot image classification and Whole Slide Image (WSI) analysis. Additionally, they can function as vision encoders when combined with large language models (LLMs) to support broa... | Image-text pairs generation, Vision-language models, Multi-agent collaboration | We present PathGen-1.6M, an open-source large-scale pathology dataset with 1.6M high-quality image-caption pairs, enabling the creation of powerful multimodal models for pathology analysis. | 6,633 | null | [
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103 | Training on the Test Task Confounds Evaluation and Emergence | https://openreview.net/forum?id=jOmk0uS1hl | [
"Ricardo Dominguez-Olmedo",
"Florian E. Dorner",
"Moritz Hardt"
] | Oral | We study a fundamental problem in the evaluation of large language models that we call training on the test task. Unlike wrongful practices like training on the test data, leakage, or data contamination, training on the test task is not a malpractice. Rather, the term describes a growing set of techniques to include t... | language models, benchmarking, emergence | null | 6,619 | 2407.07890 | [
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104 | Subgraph Federated Learning for Local Generalization | https://openreview.net/forum?id=cH65nS5sOz | [
"Sungwon Kim",
"Yoonho Lee",
"Yunhak Oh",
"Namkyeong Lee",
"Sukwon Yun",
"Junseok Lee",
"Sein Kim",
"Carl Yang",
"Chanyoung Park"
] | Oral | Federated Learning (FL) on graphs enables collaborative model training to enhance performance without compromising the privacy of each client. However, existing methods often overlook the mutable nature of graph data, which frequently introduces new nodes and leads to shifts in label distribution. Since they focus sole... | Graph Neural Networks, Graph Federated Learning | null | 6,521 | 2503.03995 | [
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105 | A Probabilistic Perspective on Unlearning and Alignment for Large Language Models | https://openreview.net/forum?id=51WraMid8K | [
"Yan Scholten",
"Stephan Günnemann",
"Leo Schwinn"
] | Oral | Comprehensive evaluation of Large Language Models (LLMs) is an open research problem. Existing evaluations rely on deterministic point estimates generated via greedy decoding. However, we find that deterministic evaluations fail to capture the whole output distribution of a model, yielding inaccurate estimations of mod... | Machine Unlearning, Alignment, Large Language Models | We demonstrate that existing deterministic evaluations in large language models are insufficient and propose a novel probabilistic evaluation framework that considers the whole output distribution of a model. | 6,509 | 2410.03523 | [
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106 | MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering | https://openreview.net/forum?id=6s5uXNWGIh | [
"Jun Shern Chan",
"Neil Chowdhury",
"Oliver Jaffe",
"James Aung",
"Dane Sherburn",
"Evan Mays",
"Giulio Starace",
"Kevin Liu",
"Leon Maksin",
"Tejal Patwardhan",
"Aleksander Madry",
"Lilian Weng"
] | Oral | We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering. To this end, we curate 75 ML engineering-related competitions from Kaggle, creating a diverse set of challenging tasks that test real-world ML engineering skills such as training models, preparing datasets, and... | benchmark, evals, evaluations, dataset, tasks, data science, engineering, agents, language agents, scaffold, coding, swe, mle | We introduce MLE-bench, a benchmark for measuring how well AI agents perform on machine learning engineering problems. | 6,441 | null | [
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107 | Learning Randomized Algorithms with Transformers | https://openreview.net/forum?id=UV5p3JZMjC | [
"Johannes Von Oswald",
"Seijin Kobayashi",
"Yassir Akram",
"Angelika Steger"
] | Oral | Randomization is a powerful tool that endows algorithms with remarkable properties. For instance, randomized algorithms excel in adversarial settings, often surpassing the worst-case performance of deterministic algorithms with large margins. Furthermore, their success probability can be amplified by simple strategies ... | Randomized algorithms, Learning under adversarial losses, Adversarial robustness, In-context learning algorithms | null | 6,351 | 2408.10818 | [
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108 | Data Scaling Laws in Imitation Learning for Robotic Manipulation | https://openreview.net/forum?id=pISLZG7ktL | [
"Fanqi Lin",
"Yingdong Hu",
"Pingyue Sheng",
"Chuan Wen",
"Jiacheng You",
"Yang Gao"
] | Oral | Data scaling has revolutionized fields like natural language processing and computer vision, providing models with remarkable generalization capabilities. In this paper, we investigate whether similar data scaling laws exist in robotics, particularly in robotic manipulation, and whether appropriate data scaling can yie... | Data Scaling Laws, Imitation Learning, Robotic Manipulation | null | 6,331 | 2410.18647 | [
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109 | Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series | https://openreview.net/forum?id=8zJRon6k5v | [
"Byoungwoo Park",
"Hyungi Lee",
"Juho Lee"
] | Oral | Many real-world datasets, such as healthcare, climate, and economics, are often collected as irregular time series, which poses challenges for accurate modeling. In this paper, we propose the Amortized Control of continuous State Space Model (ACSSM) for continuous dynamical modeling of time series for irregular and dis... | stochastic optimal control, variational inference, state space model, irregular time series | We propose a multi-marginal Doob's $h$-transform for irregular time series and variational inference with stochastic optimal control to approximate it. | 6,305 | 2410.05602 | [
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110 | Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates | https://openreview.net/forum?id=syThiTmWWm | [
"Xiaosen Zheng",
"Tianyu Pang",
"Chao Du",
"Qian Liu",
"Jing Jiang",
"Min Lin"
] | Oral | Automatic LLM benchmarks, such as AlpacaEval 2.0, Arena-Hard-Auto, and MT-Bench, have become popular for evaluating language models due to their cost-effectiveness and scalability compared to human evaluation. Achieving high win rates on these benchmarks can significantly boost the promotional impact of newly released ... | Large Language Models, Cheating, Automatic LLM Benchmarks | We show that null models that always return the same cheating responses can achieve high win rates on automatic LLM benchmarks. | 6,258 | 2410.07137 | [
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111 | On the Hölder Stability of Multiset and Graph Neural Networks | https://openreview.net/forum?id=P7KIGdgW8S | [
"Yair Davidson",
"Nadav Dym"
] | Oral | Extensive research efforts have been put into characterizing and constructing maximally separating multiset and graph neural networks.
However, recent empirical evidence suggests the notion of separation itself doesn't capture several interesting phenomena. On the one hand, the quality of this separation may be very w... | graph neural networks, message passing neural networks, multiset neural networks, neural network stability, expressive power, WL tests | null | 5,998 | null | [
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112 | On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding | https://openreview.net/forum?id=Xo0Q1N7CGk | [
"Dehong Xu",
"Ruiqi Gao",
"Wenhao Zhang",
"Xue-Xin Wei",
"Ying Nian Wu"
] | Oral | This paper investigates the conformal isometry hypothesis as a potential explanation for the hexagonal periodic patterns in grid cell response maps. We posit that grid cell activities form a high-dimensional vector in neural space, encoding the agent's position in 2D physical space. As the agent moves, this vector rota... | grid cells, conformal isometry, distance-preserving, position embedding, representation learning | We investigate the conformal isometry hypothesis that leads to the emergence of hexagon periodic patterns in grid cells, showing that learning a maximally distance-preserving position embedding naturally leads to these patterns. | 5,957 | 2405.16865 | [
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113 | Combatting Dimensional Collapse in LLM Pre-Training Data via Submodular File Selection | https://openreview.net/forum?id=f4gF6AIHRy | [
"Ziqing Fan",
"Siyuan Du",
"Shengchao Hu",
"Pingjie Wang",
"Li Shen",
"Ya Zhang",
"Dacheng Tao",
"Yanfeng Wang"
] | Oral | Selecting high-quality pre-training data for large language models (LLMs) is crucial for enhancing their overall performance under limited computation budget, improving both training and sample efficiency. Recent advancements in file selection primarily rely on using an existing or trained proxy model to assess the sim... | file selection, large language model, pre-training, submodular optimization | null | 5,918 | null | [
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114 | Population Transformer: Learning Population-level Representations of Neural Activity | https://openreview.net/forum?id=FVuqJt3c4L | [
"Geeling Chau",
"Christopher Wang",
"Sabera J Talukder",
"Vighnesh Subramaniam",
"Saraswati Soedarmadji",
"Yisong Yue",
"Boris Katz",
"Andrei Barbu"
] | Oral | We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address key challenges in scaling models with neural time-series data, namely, sparse and variable electrode distribution across subjects and datasets. The Population Transformer (PopT) st... | representation learning, neuroscience, self supervised learning | Representation learning of neural data | 5,882 | 2406.03044 | [
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115 | KAN: Kolmogorov–Arnold Networks | https://openreview.net/forum?id=Ozo7qJ5vZi | [
"Ziming Liu",
"Yixuan Wang",
"Sachin Vaidya",
"Fabian Ruehle",
"James Halverson",
"Marin Soljacic",
"Thomas Y. Hou",
"Max Tegmark"
] | Oral | Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons''), KANs have learnable activation functions on edges ("weights''). KANs have no linear weight... | Kolmogorov-Arnold networks, Kolmogorov-Arnold representation theorem, learnable activation functions, interpretability, AI + Science | Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). | 5,796 | null | [
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116 | Problem-Parameter-Free Federated Learning | https://openreview.net/forum?id=ZuazHmXTns | [
"Wenjing Yan",
"Kai Zhang",
"Xiaolu Wang",
"Xuanyu Cao"
] | Oral | Federated learning (FL) has garnered significant attention from academia and industry in recent years due to its advantages in data privacy, scalability, and communication efficiency. However, current FL algorithms face a critical limitation: their performance heavily depends on meticulously tuned hyperparameters, part... | Adaptive federated learning, problem-parameter free, arbitrary data heterogeneity, adaptive stepsize | null | 5,729 | null | [
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117 | SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups | https://openreview.net/forum?id=EO8xpnW7aX | [
"Yongxing Zhang",
"Donglin Yang",
"Renjie Liao"
] | Oral | The group of permutations $S_n$, also known as the finite symmetric groups, are essential in fields such as combinatorics, physics, and chemistry. However, learning a probability distribution over $S_n$ poses significant challenges due to its intractable size and discrete nature. In this paper, we introduce *SymmetricD... | Finite Symmetric Groups, Discrete Diffusion, Permutations, Riffle Shuffles, Plackett-Luce Distribution, Sorting, Jigsaw Puzzle | null | 5,686 | 2410.02942 | [
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118 | Language Representations Can be What Recommenders Need: Findings and Potentials | https://openreview.net/forum?id=eIJfOIMN9z | [
"Leheng Sheng",
"An Zhang",
"Yi Zhang",
"Yuxin Chen",
"Xiang Wang",
"Tat-Seng Chua"
] | Oral | Recent studies empirically indicate that language models (LMs) encode rich world knowledge beyond mere semantics, attracting significant attention across various fields.
However, in the recommendation domain, it remains uncertain whether LMs implicitly encode user preference information. Contrary to prevailing understa... | Collaborative filtering, Language-representation-based recommendation, Language models, Language model representations | null | 5,613 | 2407.05441 | [
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119 | HiRA: Parameter-Efficient Hadamard High-Rank Adaptation for Large Language Models | https://openreview.net/forum?id=TwJrTz9cRS | [
"Qiushi Huang",
"Tom Ko",
"Zhan Zhuang",
"Lilian Tang",
"Yu Zhang"
] | Oral | We propose Hadamard High-Rank Adaptation (HiRA), a parameter-efficient fine-tuning (PEFT) method that enhances the adaptability of Large Language Models (LLMs). While Low-rank Adaptation (LoRA) is widely used to reduce resource demands, its low-rank updates may limit its expressiveness for new tasks. HiRA addresses thi... | Parametric-efficient fine-tuning, Large Language Model | null | 5,572 | null | [
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120 | A Theoretically-Principled Sparse, Connected, and Rigid Graph Representation of Molecules | https://openreview.net/forum?id=OIvg3MqWX2 | [
"Shih-Hsin Wang",
"Yuhao Huang",
"Justin M. Baker",
"Yuan-En Sun",
"Qi Tang",
"Bao Wang"
] | Oral | Graph neural networks (GNNs) -- learn graph representations by exploiting graph's sparsity, connectivity, and symmetries -- have become indispensable for learning geometric data like molecules. However, the most used graphs (e.g., radial cutoff graphs) in molecular modeling lack theoretical guarantees for achieving con... | Graph representation, sparsity, connectivity, rigidity, molecules, learning | We introduce a new sparse, connected, and rigid graph representation for molecules. | 5,512 | null | [
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121 | How much of my dataset did you use? Quantitative Data Usage Inference in Machine Learning | https://openreview.net/forum?id=EUSkm2sVJ6 | [
"Yao Tong",
"Jiayuan Ye",
"Sajjad Zarifzadeh",
"Reza Shokri"
] | Oral | How much of my data was used to train a machine learning model? This is a critical question for data owners assessing the risk of unauthorized usage of their data to train models. However, previous work mistakenly treats this as a binary problem—inferring whether all-or-none or any-or-none of the data was used—which is... | Machine Learning, Privacy, Dataset Usage Inference, Dataset Ownership, Membership Inference Attack, Dataset Copyright | The first method to quantitatively and non-binarily answer the question ``How much has a dataset been used in the training of a given model?'' | 5,454 | null | [
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122 | LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior | https://openreview.net/forum?id=Wr3UuEx72f | [
"Hanyu Wang",
"Saksham Suri",
"Yixuan Ren",
"Hao Chen",
"Abhinav Shrivastava"
] | Oral | We present LARP, a novel video tokenizer designed to overcome limitations in current video tokenization methods for autoregressive (AR) generative models. Unlike traditional patchwise tokenizers that directly encode local visual patches into discrete tokens, LARP introduces a holistic tokenization scheme that gathers i... | Video Generation, Visual Tokenization | A holistic video tokenizer with a learned autoregressive generative prior. | 5,428 | 2410.21264 | [
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... |
123 | MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection | https://openreview.net/forum?id=Y6aHdDNQYD | [
"Zhuoxiao Chen",
"Junjie Meng",
"Mahsa Baktashmotlagh",
"Yonggang Zhang",
"Zi Huang",
"Yadan Luo"
] | Oral | LiDAR-based 3D object detection is crucial for various applications but often experiences performance degradation in real-world deployments due to domain shifts. While most studies focus on cross-dataset shifts, such as changes in environments and object geometries, practical corruptions from sensor variations and weat... | Test-Time Adaptation, 3D Object Detection | null | 5,340 | 2406.14878 | [
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124 | Synthetic continued pretraining | https://openreview.net/forum?id=07yvxWDSla | [
"Zitong Yang",
"Neil Band",
"Shuangping Li",
"Emmanuel Candes",
"Tatsunori Hashimoto"
] | Oral | Pretraining on large-scale, unstructured internet text enables language models to acquire a significant amount of world knowledge.
However, this knowledge acquisition is data-inefficient---to learn a fact, models must be trained on hundreds to thousands of diverse representations of it.
This poses a challenge when adap... | large language model, synthetic data, continued pretraining | null | 5,336 | 2409.07431 | [
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125 | EmbodiedSAM: Online Segment Any 3D Thing in Real Time | https://openreview.net/forum?id=XFYUwIyTxQ | [
"Xiuwei Xu",
"Huangxing Chen",
"Linqing Zhao",
"Ziwei Wang",
"Jie Zhou",
"Jiwen Lu"
] | Oral | Embodied tasks require the agent to fully understand 3D scenes simultaneously with its exploration, so an online, real-time, fine-grained and highly-generalized 3D perception model is desperately needed. Since high-quality 3D data is limited, directly training such a model in 3D is infeasible. Meanwhile, vision foundat... | 3d instance segmentation; online 3d scene segmentation | We presented EmbodiedSAM, an efficient framework that leverages vision foundation models for online, real-time, fine-grained and generalized 3D instance segmentation. | 5,293 | 2408.11811 | [
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126 | Tractable Multi-Agent Reinforcement Learning through Behavioral Economics | https://openreview.net/forum?id=stUKwWBuBm | [
"Eric Mazumdar",
"Kishan Panaganti",
"Laixi Shi"
] | Oral | A significant roadblock to the development of principled multi-agent reinforcement learning (MARL) algorithms is the fact that desired solution concepts like Nash equilibria may be intractable to compute. We show how one can overcome this obstacle by introducing concepts from behavioral economics into MARL. To do so, w... | behavioral economics, risk-aversion, multi-agent reinforcement learning, quantal response, bounded rationality | By incorporating risk aversion and bounded rationality into agents' decision-making processes, we introduced a computationally tractable equilibria class for matrix and Markov games which aligns with observed human behaviors. | 5,242 | null | [
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127 | Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent | https://openreview.net/forum?id=sbG8qhMjkZ | [
"Sayan Banerjee",
"Krishna Balasubramanian",
"PROMIT GHOSAL"
] | Oral | We provide finite-particle convergence rates for the Stein Variational Gradient Descent (SVGD) algorithm in the Kernelized Stein Discrepancy ($\KSD$) and Wasserstein-2 metrics. Our key insight is that the time derivative of the relative entropy between the joint density of $N$ particle locations and the $N$-fold produc... | Stein Variational Gradient Descent, Non-asymptotic Rates, Variational Inference | Near-optimal finite-particle, discrete-time rates for SVGD | 5,180 | 2409.08469 | [
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128 | Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning | https://openreview.net/forum?id=gc8QAQfXv6 | [
"Gangwei Jiang",
"Caigao JIANG",
"Zhaoyi Li",
"Siqiao Xue",
"JUN ZHOU",
"Linqi Song",
"Defu Lian",
"Ying Wei"
] | Oral | Catastrophic forgetting (CF) poses a significant challenge in machine learning, where a model forgets previously learned information upon learning new tasks.
Despite the advanced capabilities of Large Language Models (LLMs), they continue to face challenges with CF during continual learning. The majority of existing r... | Catastrophic forgetting; Large language model; Instruction tuning | null | 5,157 | 2502.11019 | [
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129 | One Step Diffusion via Shortcut Models | https://openreview.net/forum?id=OlzB6LnXcS | [
"Kevin Frans",
"Danijar Hafner",
"Sergey Levine",
"Pieter Abbeel"
] | Oral | Diffusion models and flow matching models have enabled generating diverse and realistic images by learning to transfer noise to data. However, sampling from these models involves iterative denoising over many neural network passes, making generation slow and expensive. Previous approaches for speeding up sampling requi... | diffusion, flow-matching, fast inference, distillation | null | 5,115 | 2410.12557 | [
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130 | Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment | https://openreview.net/forum?id=mtSSFiqW6y | [
"Gregor Bachmann",
"Sotiris Anagnostidis",
"Albert Pumarola",
"Markos Georgopoulos",
"Artsiom Sanakoyeu",
"Yuming Du",
"Edgar Schönfeld",
"Ali Thabet",
"Jonas K Kohler"
] | Oral | The performance of large language models (LLMs) is closely linked to their underlying size, leading to ever-growing networks and hence slower inference. Speculative decoding has been proposed as a technique to accelerate autoregressive generation, leveraging a fast draft model to propose candidate tokens, which are the... | LLM inference, speculative decoding | null | 5,114 | 2501.19309 | [
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131 | Robustness Inspired Graph Backdoor Defense | https://openreview.net/forum?id=trKNi4IUiP | [
"Zhiwei Zhang",
"Minhua Lin",
"Junjie Xu",
"Zongyu Wu",
"Enyan Dai",
"Suhang Wang"
] | Oral | Graph Neural Networks (GNNs) have achieved promising results in tasks such as node classification and graph classification. However, recent studies reveal that GNNs are vulnerable to backdoor attacks, posing a significant threat to their real-world adoption. Despite initial efforts to defend against specific graph back... | Backdoor Defense, Graph Neural Network | null | 5,103 | 2406.09836 | [
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... |
132 | Proxy Denoising for Source-Free Domain Adaptation | https://openreview.net/forum?id=FIj9IEPCKr | [
"Song Tang",
"Wenxin Su",
"Yan Gan",
"Mao Ye",
"Jianwei Dr. Zhang",
"Xiatian Zhu"
] | Oral | Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to an unlabeled target domain with no access to the source data. Inspired by the success of large Vision-Language (ViL) models in many applications, the latest research has validated ViL's benefit for SFDA by using their predictions as pseudo... | Domain adaptation, source-free, multimodal proxy space, proxy confidence theory | null | 5,075 | 2406.01658 | [
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0.014... |
133 | Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models | https://openreview.net/forum?id=tc90LV0yRL | [
"Andy K Zhang",
"Neil Perry",
"Riya Dulepet",
"Joey Ji",
"Celeste Menders",
"Justin W Lin",
"Eliot Jones",
"Gashon Hussein",
"Samantha Liu",
"Donovan Julian Jasper",
"Pura Peetathawatchai",
"Ari Glenn",
"Vikram Sivashankar",
"Daniel Zamoshchin",
"Leo Glikbarg",
"Derek Askaryar",
"Hao... | Oral | Language Model (LM) agents for cybersecurity that are capable of autonomously identifying vulnerabilities and executing exploits have potential to cause real-world impact. Policymakers, model providers, and researchers in the AI and cybersecurity communities are interested in quantifying the capabilities of such agents... | Language Model Agents, Benchmark, Cybersecurity, Risk | Cybench is a cybersecurity agent benchmark with 40 professional-level Capture the Flag tasks that are recent, meaningful, and difficult with subtasks. | 5,074 | 2408.08926 | [
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-0.0... |
134 | Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation | https://openreview.net/forum?id=CRmiX0v16e | [
"Mohamed El Amine Boudjoghra",
"Angela Dai",
"Jean Lahoud",
"Hisham Cholakkal",
"Rao Muhammad Anwer",
"Salman Khan",
"Fahad Shahbaz Khan"
] | Oral | Recent works on open-vocabulary 3D instance segmentation show strong promise but at the cost of slow inference speed and high computation requirements. This high computation cost is typically due to their heavy reliance on aggregated clip features from multi-view, which require computationally expensive 2D foundation m... | Open Vocabulary, 3D point cloud instance segmentation | null | 4,987 | 2406.02548 | [
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0.0... |
135 | Safety Alignment Should be Made More Than Just a Few Tokens Deep | https://openreview.net/forum?id=6Mxhg9PtDE | [
"Xiangyu Qi",
"Ashwinee Panda",
"Kaifeng Lyu",
"Xiao Ma",
"Subhrajit Roy",
"Ahmad Beirami",
"Prateek Mittal",
"Peter Henderson"
] | Oral | The safety alignment of current Large Language Models (LLMs) is vulnerable. Simple attacks, or even benign fine-tuning, can jailbreak aligned models. We note that many of these vulnerabilities are related to a shared underlying issue: safety alignment can take shortcuts, wherein the alignment adapts a model's generativ... | Safety Alignment, AI Safety, LLM | We identify an underlying problem (shallow safety alignment) tha makes current safety alignment vulnerable, and we also propose approaches for mitigations. | 4,914 | 2406.05946 | [
-0.020647823810577393,
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0.... |
136 | On the Identification of Temporal Causal Representation with Instantaneous Dependence | https://openreview.net/forum?id=2efNHgYRvM | [
"Zijian Li",
"Yifan Shen",
"Kaitao Zheng",
"Ruichu Cai",
"Xiangchen Song",
"Mingming Gong",
"Guangyi Chen",
"Kun Zhang"
] | Oral | Temporally causal representation learning aims to identify the latent causal process from time series observations, but most methods require the assumption that the latent causal processes do not have instantaneous relations. Although some recent methods achieve identifiability in the instantaneous causality case, they... | Causal Representation Learning, Instantaneous Dependency, Identification | null | 4,912 | 2405.15325 | [
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0.003... |
137 | WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct | https://openreview.net/forum?id=mMPMHWOdOy | [
"Haipeng Luo",
"Qingfeng Sun",
"Can Xu",
"Pu Zhao",
"Jian-Guang Lou",
"Chongyang Tao",
"Xiubo Geng",
"Qingwei Lin",
"Shifeng Chen",
"Yansong Tang",
"Dongmei Zhang"
] | Oral | Large language models (LLMs), such as GPT-4, have shown remarkable performance in natural language processing (NLP) tasks, including challenging mathematical reasoning. However, most existing open-source models are only pre-trained on large-scale internet data and without math-related optimization. In this paper, we pr... | Mathematical Reasoning, Evol-Instruct, Reinforcement Learning | null | 4,894 | 2308.09583 | [
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138 | Faster Cascades via Speculative Decoding | https://openreview.net/forum?id=vo9t20wsmd | [
"Harikrishna Narasimhan",
"Wittawat Jitkrittum",
"Ankit Singh Rawat",
"Seungyeon Kim",
"Neha Gupta",
"Aditya Krishna Menon",
"Sanjiv Kumar"
] | Oral | Cascades and speculative decoding are two common approaches to improving language models' inference efficiency. Both approaches interleave two models, but via fundamentally distinct mechanisms: deferral rule that invokes the larger model only for “hard” inputs, while speculative decoding uses speculative execution to... | Cascades, Speculative Decoding, Speculative execution, LLM, Inference, Adaptive Inference | Faster language model cascades through the use of speculative execution | 4,871 | 2405.19261 | [
-0.01441245898604393,
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0.052057038992643356,
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0.03017451986670494,
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139 | The Hidden Cost of Waiting for Accurate Predictions | https://openreview.net/forum?id=A3YUPeJTNR | [
"Ali Shirali",
"Ariel D. Procaccia",
"Rediet Abebe"
] | Oral | Algorithmic predictions are increasingly informing societal resource allocations by identifying individuals for targeting. Policymakers often build these systems with the assumption that by gathering more observations on individuals, they can improve predictive accuracy and, consequently, allocation efficiency. An over... | Algorithmic Decision Making, Prediction, Resource Allocation, Social Welfare, Limits of Prediction | null | 4,828 | 2503.00650 | [
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-... |
140 | Learning Dynamics of LLM Finetuning | https://openreview.net/forum?id=tPNHOoZFl9 | [
"Yi Ren",
"Danica J. Sutherland"
] | Oral | Learning dynamics, which describes how the learning of specific training examples influences the model's predictions on other examples,
gives us a powerful tool for understanding the behavior of deep learning systems. We study the learning dynamics of large language models during different types of finetuning, by anal... | Learning dynamics, LLM, finetuning, DPO | The paper propose a novel learning dynamics framework to understand LLM's behavior during finetuning (e.g., SFT, DPO, and other variants). Some counter-intuitive behavior can be well explained by the proposed framework. | 4,818 | 2407.10490 | [
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0.0012... |
141 | Root Cause Analysis of Anomalies in Multivariate Time Series through Granger Causal Discovery | https://openreview.net/forum?id=k38Th3x4d9 | [
"Xiao Han",
"Saima Absar",
"Lu Zhang",
"Shuhan Yuan"
] | Oral | Identifying the root causes of anomalies in multivariate time series is challenging due to the complex dependencies among the series. In this paper, we propose a comprehensive approach called AERCA that inherently integrates Granger causal discovery with root cause analysis. By defining anomalies as interventions on th... | root cause analysis, Granger causality, multivariate time series | null | 4,815 | null | [
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... |
142 | ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids | https://openreview.net/forum?id=0ctvBgKFgc | [
"Hannes Stark",
"Bowen Jing",
"Tomas Geffner",
"Jason Yim",
"Tommi Jaakkola",
"Arash Vahdat",
"Karsten Kreis"
] | Oral | We develop ProtComposer to generate protein structures conditioned on spatial protein layouts that are specified via a set of 3D ellipsoids capturing substructure shapes and semantics. At inference time, we condition on ellipsoids that are hand-constructed, extracted from existing proteins, or from a statistical model,... | protein design, diffusion model, controllable generation, drug discovery, proteins, biology | We develop a framework to generate protein structures conditioned on spatial protein layouts that are specified via a set of 3D ellipsoids. | 4,802 | 2503.05025 | [
-0.012907739728689194,
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143 | More RLHF, More Trust? On The Impact of Preference Alignment On Trustworthiness | https://openreview.net/forum?id=FpiCLJrSW8 | [
"Aaron Jiaxun Li",
"Satyapriya Krishna",
"Himabindu Lakkaraju"
] | Oral | The trustworthiness of Large Language Models (LLMs) refers to the extent to which their outputs are reliable, safe, and ethically aligned, and it has become a crucial consideration alongside their cognitive performance. In practice, Reinforcement Learning From Human Feedback (RLHF) has been widely used to align LLMs wi... | Large Language Model, Trustworthy ML, Data Attribution | Evaluating the Impact of RLHF on Trustworthiness Aspects | 4,767 | 2404.18870 | [
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144 | Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects | https://openreview.net/forum?id=7BLXhmWvwF | [
"Tai Hoang",
"Huy Le",
"Philipp Becker",
"Vien Anh Ngo",
"Gerhard Neumann"
] | Oral | Manipulating objects with varying geometries and deformable objects is a major challenge in robotics. Tasks such as insertion with different objects or cloth hanging require precise control and effective modelling of complex dynamics. In this work, we frame this problem through the lens of a heterogeneous graph that co... | Robotic Manipulation, Equivariance, Graph Neural Networks, Reinforcement Learning, Deformable Objects | Geometry-aware RL with heterogeneous SE(3) equivariant back-bone policy for robotic manipulation | 4,674 | 2502.07005 | [
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145 | Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity | https://openreview.net/forum?id=EzjsoomYEb | [
"Yam Eitan",
"Yoav Gelberg",
"Guy Bar-Shalom",
"Fabrizio Frasca",
"Michael M. Bronstein",
"Haggai Maron"
] | Oral | Topological deep learning (TDL) is a rapidly growing field that seeks to leverage topological structure in data and facilitate learning from data supported on topological objects, ranging from molecules to 3D shapes. Most TDL architectures can be unified under the framework of higher-order message-passing (HOMP), which... | Topological Deep Learning, Message Passing, Higher Order Message Passing, Expressivity, Graph Neural Networks, GNNs, Topology, Homology, Symmetry | null | 4,548 | 2408.05486 | [
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146 | Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency | https://openreview.net/forum?id=weM4YBicIP | [
"Jianwen Jiang",
"Chao Liang",
"Jiaqi Yang",
"Gaojie Lin",
"Tianyun Zhong",
"Yanbo Zheng"
] | Oral | With the introduction of video diffusion model, audio-conditioned human video generation has recently achieved significant breakthroughs in both the naturalness of motion and the synthesis of portrait details. Due to the limited control of audio signals in driving human motion, existing methods often add auxiliary spat... | Diffusion Model, Avatar, Portrait Animation, Audio-Condition Video Generation | We propose Loopy, an end-to-end audio-conditioned video diffusion model that uses long-term motion information to learn natural motions and improve audio-portrait correlation, eliminating motion constraints and delivering high-quality results. | 4,292 | 2409.02634 | [
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147 | CyberHost: A One-stage Diffusion Framework for Audio-driven Talking Body Generation | https://openreview.net/forum?id=vaEPihQsAA | [
"Gaojie Lin",
"Jianwen Jiang",
"Chao Liang",
"Tianyun Zhong",
"Jiaqi Yang",
"Zerong Zheng",
"Yanbo Zheng"
] | Oral | Diffusion-based video generation technology has advanced significantly, catalyzing a proliferation of research in human animation. While breakthroughs have been made in driving human animation through various modalities for portraits, most of current solutions for human body animation still focus on video-driven method... | Audio-driven Human Animation.+Diffusion Model.+Generative Model.+Human Video Generation | We propose a one-stage audio-driven talking body generation framework, CyberHost, designed to produce human videos that match the input audio with high expressiveness and realism. | 4,230 | null | [
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148 | Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model | https://openreview.net/forum?id=SI2hI0frk6 | [
"Chunting Zhou",
"LILI YU",
"Arun Babu",
"Kushal Tirumala",
"Michihiro Yasunaga",
"Leonid Shamis",
"Jacob Kahn",
"Xuezhe Ma",
"Luke Zettlemoyer",
"Omer Levy"
] | Oral | We introduce Transfusion, a recipe for training a multi-modal model over discrete and continuous data.
Transfusion combines the language modeling loss function (next token prediction) with diffusion to train a single transformer over mixed-modality sequences.
We pretrain multiple Transfusion models up to 7B parameters ... | multimodal foundation model, multimodal generation and understanding, diffusion, next token prediction | Transfusion is a recipe for training a multi-modal model over discrete and continuous data. | 4,134 | 2408.11039 | [
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149 | MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts | https://openreview.net/forum?id=t7P5BUKcYv | [
"Peng Jin",
"Bo Zhu",
"Li Yuan",
"Shuicheng YAN"
] | Oral | In this work, we aim to simultaneously enhance the effectiveness and efficiency of Mixture-of-Experts (MoE) methods. To achieve this, we propose MoE++, a general and heterogeneous MoE framework that integrates both Feed-Forward Network (FFN) and zero-computation experts. Specifically, we introduce three types of zero-c... | Mixture of Experts, Large Language Models, Efficient Foundation Models | We propose MoE++, a general and heterogeneous mixture-of-experts framework that achieves better performance while delivering 1.1$\sim$2.1$\times$ expert forward throughput compared to a vanilla MoE model of the same size. | 4,125 | 2410.07348 | [
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150 | Compositional Entailment Learning for Hyperbolic Vision-Language Models | https://openreview.net/forum?id=3i13Gev2hV | [
"Avik Pal",
"Max van Spengler",
"Guido Maria D'Amely di Melendugno",
"Alessandro Flaborea",
"Fabio Galasso",
"Pascal Mettes"
] | Oral | Image-text representation learning forms a cornerstone in vision-language models, where pairs of images and textual descriptions are contrastively aligned in a shared embedding space. Since visual and textual concepts are naturally hierarchical, recent work has shown that hyperbolic space can serve as a high-potential ... | Vision-Language Models, Hyperbolic Geometry, Representation Learning, CLIP | We explore the benefits brought in when using visual-semantic compositional hierarchies for learning hyperbolic representations through unsupervised contrastive training. | 4,111 | 2410.06912 | [
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151 | Advantage Alignment Algorithms | https://openreview.net/forum?id=QFO1asgas2 | [
"Juan Agustin Duque",
"Milad Aghajohari",
"Tim Cooijmans",
"razvan ciuca",
"Tianyu Zhang",
"Gauthier Gidel",
"Aaron Courville"
] | Oral | Artificially intelligent agents are increasingly being integrated into human decision-making: from large language model (LLM) assistants to autonomous vehicles. These systems often optimize their individual objective, leading to conflicts, particularly in general-sum games where naive reinforcement learning agents empi... | Multi-agent Reinforcement Learning, Opponent Shaping, Social Dilemmas, General-Sum Games | We introduce Advantage Alignment, a new family of algorithms for opponent shaping in general-sum games, designed to promote cooperation and avoid suboptimal outcomes. | 3,875 | 2406.14662 | [
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152 | Scaling In-the-Wild Training for Diffusion-based Illumination Harmonization and Editing by Imposing Consistent Light Transport | https://openreview.net/forum?id=u1cQYxRI1H | [
"Lvmin Zhang",
"Anyi Rao",
"Maneesh Agrawala"
] | Oral | Diffusion-based image generators are becoming unique methods for illumination harmonization and editing. The current bottleneck in scaling up the training of diffusion-based illumination editing models is mainly in the difficulty of preserving the underlying image details and maintaining intrinsic properties, such as a... | diffusion model, illumination editing, image editing | Diffusion-based image illumination harmonization and editing model | 3,821 | null | [
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0.00... |
153 | AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models | https://openreview.net/forum?id=HvSytvg3Jh | [
"Junfeng Fang",
"Houcheng Jiang",
"Kun Wang",
"Yunshan Ma",
"Jie Shi",
"Xiang Wang",
"Xiangnan He",
"Tat-Seng Chua"
] | Oral | Large language models (LLMs) often exhibit hallucinations, producing incorrect or outdated knowledge. Hence, model editing methods have emerged to enable targeted knowledge updates. To achieve this, a prevailing paradigm is the locating-then-editing approach, which first locates influential parameters and then edits ... | Model Editing, Null-Space, Large Language Model | We propose a novel model editing method named AlphaEdit to minimize the disruption to the preserved knowledge during editing. | 3,792 | 2410.02355 | [
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154 | DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL | https://openreview.net/forum?id=9pW2J49flQ | [
"Mathias Jackermeier",
"Alessandro Abate"
] | Oral | Linear temporal logic (LTL) has recently been adopted as a powerful formalism for specifying complex, temporally extended tasks in multi-task reinforcement learning (RL). However, learning policies that efficiently satisfy arbitrary specifications not observed during training remains a challenging problem. Existing app... | reinforcement learning, linear temporal logic, ltl, generalization | null | 3,756 | null | [
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155 | On the Role of Attention Heads in Large Language Model Safety | https://openreview.net/forum?id=h0Ak8A5yqw | [
"Zhenhong Zhou",
"Haiyang Yu",
"Xinghua Zhang",
"Rongwu Xu",
"Fei Huang",
"Kun Wang",
"Yang Liu",
"Junfeng Fang",
"Yongbin Li"
] | Oral | Large language models (LLMs) achieve state-of-the-art performance on multiple language tasks, yet their safety guardrails can be circumvented, leading to harmful generations. In light of this, recent research on safety mechanisms has emerged, revealing that when safety representations or component are suppressed, the s... | interpretability, large language model, multi-head attention, safety, harmful content | We identify safety-critical attention heads in large language models, and when these heads are ablated, the model safety is significantly compromised. | 3,741 | 2410.13708 | [
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156 | Influence Functions for Scalable Data Attribution in Diffusion Models | https://openreview.net/forum?id=esYrEndGsr | [
"Bruno Kacper Mlodozeniec",
"Runa Eschenhagen",
"Juhan Bae",
"Alexander Immer",
"David Krueger",
"Richard E. Turner"
] | Oral | Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. In this paper, we aim to help address such challenges in diffusion models by extending influence functions. Influence function-based data attribu... | diffusion models, influence functions, Generalised Gauss Newton, GGN, data attribution, Hessian approximation, interpretability, curvature, Kronecker-Factored Approximate Curvature, K-FAC | We present a method for attributing the influence of training data on diffusion model’s output by adapting influence functions and a KFAC approximation for diffusion models, and we explore what measurements we want to attribute for in the first place | 3,597 | 2410.13850 | [
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0.0... |
157 | Second-Order Min-Max Optimization with Lazy Hessians | https://openreview.net/forum?id=ijbA5swmoK | [
"Lesi Chen",
"Chengchang Liu",
"Jingzhao Zhang"
] | Oral | This paper studies second-order methods for convex-concave minimax optimization.
Monteiro & Svaiter (2012) proposed a method to solve the problem with an optimal iteration complexity of
$\mathcal{O}(\epsilon^{-3/2})$ to find an $\epsilon$-saddle point. However, it is unclear whether the
computational complexity, $... | min-max optimization; second-order methods; computational complexity | We propose novel second-order methods for min-max optimization that are provably better than existing optimal methods | 3,596 | 2410.09568 | [
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0.00... |
158 | Composing Unbalanced Flows for Flexible Docking and Relaxation | https://openreview.net/forum?id=gHLWTzKiZV | [
"Gabriele Corso",
"Vignesh Ram Somnath",
"Noah Getz",
"Regina Barzilay",
"Tommi Jaakkola",
"Andreas Krause"
] | Oral | Diffusion models have emerged as a successful approach for molecular docking, but they often cannot model protein flexibility or generate nonphysical poses. We argue that both these challenges can be tackled by framing the problem as a transport between distributions. Still, existing paradigms lack the flexibility to d... | molecular docking, flow matching, structure relaxation, unbalanced transport | A new generalized flow matching paradigm and its applications to flexible docking and relaxation | 3,566 | null | [
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0.05790340527892113,
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159 | Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks | https://openreview.net/forum?id=uKZdlihDDn | [
"Mario Lino Valencia",
"Tobias Pfaff",
"Nils Thuerey"
] | Oral | Physical systems with complex unsteady dynamics, such as fluid flows, are often poorly represented by a single mean solution. For many practical applications, it is crucial to access the full distribution of possible states, from which relevant statistics (e.g., RMS and two-point correlations) can be derived. Here, we ... | Graph Neural Networks, Diffusion Models, Physics Simulations | We propose an efficient graph-based latent diffusion model, which allows us to directly sample unsteady flow states from their equilibrium distribution given a mesh discretisation of the system and its physical parameters. | 3,559 | null | [
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0.04272628203034401,
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... |
160 | Training Language Models to Self-Correct via Reinforcement Learning | https://openreview.net/forum?id=CjwERcAU7w | [
"Aviral Kumar",
"Vincent Zhuang",
"Rishabh Agarwal",
"Yi Su",
"John D Co-Reyes",
"Avi Singh",
"Kate Baumli",
"Shariq Iqbal",
"Colton Bishop",
"Rebecca Roelofs",
"Lei M Zhang",
"Kay McKinney",
"Disha Shrivastava",
"Cosmin Paduraru",
"George Tucker",
"Doina Precup",
"Feryal Behbahani",... | Oral | Self-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffective in modern LLMs. Current methods for training self-correction typically depend on either multiple models, a more advanced model, or additional forms of supervision. To address t... | language models, reinforcement learning | null | 3,518 | 2409.12917 | [
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0.016152560710906982,
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161 | AI as Humanity’s Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text | https://openreview.net/forum?id=ilOEOIqolQ | [
"Ximing Lu",
"Melanie Sclar",
"Skyler Hallinan",
"Niloofar Mireshghallah",
"Jiacheng Liu",
"Seungju Han",
"Allyson Ettinger",
"Liwei Jiang",
"Khyathi Chandu",
"Nouha Dziri",
"Yejin Choi"
] | Oral | Creativity has long been considered one of the most difficult aspect of human intelligence for AI to mimic. However, the rise of Large Language Models (LLMs), like ChatGPT, has raised questions about whether AI can match or even surpass human creativity. We present CREATIVITY INDEX as the first step to quantify the lin... | Machine Creativity, Large Language Model, Science of LLM, Machine Text Detection | We present CREATIVITY INDEX, a metric that quantifies the creativity of a text by reconstructing it from existing web snippets, supported by a novel dynamic programming algorithm, DJ SEARCH, for efficient computation. | 3,478 | null | [
-0.009438997134566307,
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162 | Comparing noisy neural population dynamics using optimal transport distances | https://openreview.net/forum?id=cNmu0hZ4CL | [
"Amin Nejatbakhsh",
"Victor Geadah",
"Alex H Williams",
"David Lipshutz"
] | Oral | Biological and artificial neural systems form high-dimensional neural representations that underpin their computational capabilities. Methods for quantifying geometric similarity in neural representations have become a popular tool for identifying computational principles that are potentially shared across neural syste... | Representational similarity, shape metrics, optimal transport, Wasserstein distance | We propose using optimal transport distances on stochastic processes to compare noisy neural trajectories. | 3,439 | 2412.14421 | [
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163 | Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport | https://openreview.net/forum?id=gQlxd3Mtru | [
"Zhenyi Zhang",
"Tiejun Li",
"Peijie Zhou"
] | Oral | Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning. Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from obse... | optimal transport, Schrödinger bridge, trajectory inference, single-cell | null | 3,337 | 2410.00844 | [
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164 | Prioritized Generative Replay | https://openreview.net/forum?id=5IkDAfabuo | [
"Renhao Wang",
"Kevin Frans",
"Pieter Abbeel",
"Sergey Levine",
"Alexei A Efros"
] | Oral | Sample-efficient online reinforcement learning often uses replay buffers to store experience for reuse when updating the value function.
However, uniform replay is inefficient, since certain classes of transitions can be more relevant to learning. While prioritization of more useful samples is helpful, this strategy c... | online learning, model-based reinforcement learning, generative modeling, synthetic data, continual learning | We construct a conditional generative model of an agent's online memory, allowing us to replay high-priority data at large quantities to accelerate training of online RL agents. | 3,226 | 2410.18082 | [
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165 | The Geometry of Categorical and Hierarchical Concepts in Large Language Models | https://openreview.net/forum?id=bVTM2QKYuA | [
"Kiho Park",
"Yo Joong Choe",
"Yibo Jiang",
"Victor Veitch"
] | Oral | The linear representation hypothesis is the informal idea that semantic concepts are encoded as linear directions in the representation spaces of large language models (LLMs). Previous work has shown how to make this notion precise for representing binary concepts that have natural contrasts (e.g., {male, female}) as _... | categorical concepts, hierarchical concepts, linear representation hypothesis, causal inner product, interpretability | We extend the linear representation hypothesis to general concepts and show that hierarchical relationships are encoded as orthogonality. | 3,176 | 2406.01506 | [
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166 | Generator Matching: Generative modeling with arbitrary Markov processes | https://openreview.net/forum?id=RuP17cJtZo | [
"Peter Holderrieth",
"Marton Havasi",
"Jason Yim",
"Neta Shaul",
"Itai Gat",
"Tommi Jaakkola",
"Brian Karrer",
"Ricky T. Q. Chen",
"Yaron Lipman"
] | Oral | We introduce Generator Matching, a modality-agnostic framework for generative modeling using arbitrary Markov processes. Generators characterize the infinitesimal evolution of a Markov process, which we leverage for generative modeling in a similar vein to flow matching: we construct conditional generators which genera... | Flow matching, Markov process, Diffusion model, Generative Modeling | The core principles of flow matching can be vastly generalized to practically all continuous-time Markov processes using Markov generators, unifying all previous methods and opening the door to new generative models agnostic to data modality. | 3,162 | 2410.20587 | [
0.0004361197352409363,
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167 | No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images | https://openreview.net/forum?id=P4o9akekdf | [
"Botao Ye",
"Sifei Liu",
"Haofei Xu",
"Xueting Li",
"Marc Pollefeys",
"Ming-Hsuan Yang",
"Songyou Peng"
] | Oral | We introduce NoPoSplat, a feed-forward model capable of reconstructing 3D scenes parameterized by 3D Gaussians from unposed sparse multi-view images. Our model, trained exclusively with photometric loss, achieves real-time 3D Gaussian reconstruction during inference. To eliminate the need for accurate pose input during... | 3D Gaussian Splatting, Pose Free, Pose Estimation, Novel View Synthesis, 3D Reconstruction | NoPoSplat is a novel feed-forward model that reconstructs scenes from unposed images by predicting Gaussians in a canonical space, demonstrating superior performance in both novel view synthesis and pose estimation. | 3,116 | 2410.24207 | [
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168 | Variational Diffusion Posterior Sampling with Midpoint Guidance | https://openreview.net/forum?id=6EUtjXAvmj | [
"Badr MOUFAD",
"Yazid Janati",
"Lisa Bedin",
"Alain Oliviero Durmus",
"randal douc",
"Eric Moulines",
"Jimmy Olsson"
] | Oral | Diffusion models have recently shown considerable potential in solving Bayesian inverse problems when used as priors. However, sampling from the resulting denoising posterior distributions remains a challenge as it involves intractable terms. To tackle this issue, state-of-the-art approaches formulate the problem as th... | Diffusion models, Inverse problems, posterior sampling | null | 3,058 | 2410.09945 | [
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... |
169 | Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning | https://openreview.net/forum?id=25kAzqzTrz | [
"Jingyang Li",
"Jiachun Pan",
"Vincent Y. F. Tan",
"Kim-chuan Toh",
"Pan Zhou"
] | Oral | Semi-supervised learning (SSL), exemplified by FixMatch (Sohn et al., 2020), has shown significant generalization advantages over supervised learning (SL), particularly in the context of deep neural networks (DNNs). However, it is still unclear, from a theoretical standpoint, why FixMatch-like SSL algorithms generalize... | deep semi-supervised learning, generalization error, feature learning | null | 2,984 | 2410.11206 | [
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0.002... |
170 | NeuralPlane: Structured 3D Reconstruction in Planar Primitives with Neural Fields | https://openreview.net/forum?id=5UKrnKuspb | [
"Hanqiao Ye",
"Yuzhou Liu",
"Yangdong Liu",
"Shuhan Shen"
] | Oral | 3D maps assembled from planar primitives are compact and expressive in representing man-made environments. In this paper, we present **NeuralPlane**, a novel approach that explores **neural** fields for multi-view 3D **plane** reconstruction. Our method is centered upon the core idea of distilling geometric and semanti... | 3D Reconstruction, 3D Scene Understanding, Scene Abstraction, Neural Rendering | NeuralPlane rebuilds indoor scenes as arrangements of planar primitives from multi-view images. | 2,933 | null | [
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171 | Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models | https://openreview.net/forum?id=I4e82CIDxv | [
"Samuel Marks",
"Can Rager",
"Eric J Michaud",
"Yonatan Belinkov",
"David Bau",
"Aaron Mueller"
] | Oral | We introduce methods for discovering and applying **sparse feature circuits**. These are causally implicated subnetworks of human-interpretable features for explaining language model behaviors. Circuits identified in prior work consist of polysemantic and difficult-to-interpret units like attention heads or neurons, re... | Interpretability, mechanistic interpretability, circuits, spurious correlations, generalization, dictionary learning | We automatically discover circuits of interpretable components and apply them to remove sensitivity to spurious correlates | 2,718 | 2403.19647 | [
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172 | Retrieval Head Mechanistically Explains Long-Context Factuality | https://openreview.net/forum?id=EytBpUGB1Z | [
"Wenhao Wu",
"Yizhong Wang",
"Guangxuan Xiao",
"Hao Peng",
"Yao Fu"
] | Oral | Despite the recent progress in long-context language models, it remains elusive how transformer-based models exhibit the capability to retrieve relevant information from arbitrary locations within the long context. This paper aims to address this question. Our systematic investigation across a wide spectrum of models r... | Large language models, long context, interpretability, attention | We study retrieval head, a special type of attention head that mechanistically explains long-context factuality | 2,659 | 2404.15574 | [
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173 | High-Dynamic Radar Sequence Prediction for Weather Nowcasting Using Spatiotemporal Coherent Gaussian Representation | https://openreview.net/forum?id=Cjz9Xhm7sI | [
"Ziye Wang",
"Yiran Qin",
"Lin Zeng",
"Ruimao Zhang"
] | Oral | Weather nowcasting is an essential task that involves predicting future radar echo sequences based on current observations, offering significant benefits for disaster management, transportation, and urban planning. Current prediction methods are limited by training and storage efficiency, mainly focusing on 2D spatial ... | 3D Gaussian, Dynamic Reconstruction, Radar Prediction, Weather Nowcasting | null | 2,603 | 2502.14895 | [
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... |
174 | Differential Transformer | https://openreview.net/forum?id=OvoCm1gGhN | [
"Tianzhu Ye",
"Li Dong",
"Yuqing Xia",
"Yutao Sun",
"Yi Zhu",
"Gao Huang",
"Furu Wei"
] | Oral | Transformer tends to overallocate attention to irrelevant context. In this work, we introduce Diff Transformer, which amplifies attention to the relevant context while canceling noise. Specifically, the differential attention mechanism calculates attention scores as the difference between two separate softmax attention... | sequence modeling, language models, model architecture, Transformer | null | 2,557 | 2410.05258 | [
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0.0... |
175 | Open-Vocabulary Customization from CLIP via Data-Free Knowledge Distillation | https://openreview.net/forum?id=1aF2D2CPHi | [
"Yongxian Wei",
"Zixuan Hu",
"Li Shen",
"Zhenyi Wang",
"Chun Yuan",
"Dacheng Tao"
] | Oral | Vision-language models such as CLIP have demonstrated strong zero-shot performance, but their considerable size and inefficient inference limit customizable deployment for users. While knowledge distillation is a solution, it still requires the original data, which is not always available due to copyrights and privacy ... | Data-Free Learning, CLIP Model, Customization | Could we distill models from CLIP without data to meet customized tasks? | 2,525 | null | [
0.02264239639043808,
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0.027322303503751755,
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176 | Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement | https://openreview.net/forum?id=UHPnqSTBPO | [
"Jaehun Jung",
"Faeze Brahman",
"Yejin Choi"
] | Oral | We present a principled approach to provide LLM-based evaluation with a rigorous guarantee of human agreement. We first propose that a reliable evaluation method should not uncritically rely on model preferences for pairwise evaluation, but rather assess the confidence of judge models and selectively decide when to tru... | Large Language Model, LLM, LLM Judge, Evaluation, Alignment | We propose Cascaded Selective Evaluation, an LLM-as-Judge framework that dynamically selects when to trust different judge models to reduce evaluation overhead, while providing a provable guarantee of human-judge agreement. | 2,430 | 2407.18370 | [
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177 | Your Mixture-of-Experts LLM Is Secretly an Embedding Model for Free | https://openreview.net/forum?id=eFGQ97z5Cd | [
"Ziyue Li",
"Tianyi Zhou"
] | Oral | While large language models (LLMs) excel on generation tasks, their decoder-only architecture often limits their potential as embedding models if no further representation finetuning is applied. Does this contradict their claim of generalists? To answer the question, we take a closer look at Mixture-of-Experts (MoE) LL... | Mixture of Experts | null | 2,416 | 2410.10814 | [
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178 | REEF: Representation Encoding Fingerprints for Large Language Models | https://openreview.net/forum?id=SnDmPkOJ0T | [
"Jie Zhang",
"Dongrui Liu",
"Chen Qian",
"Linfeng Zhang",
"Yong Liu",
"Yu Qiao",
"Jing Shao"
] | Oral | Protecting the intellectual property of open-source Large Language Models (LLMs) is very important, because training LLMs costs extensive computational resources and data. Therefore, model owners and third parties need to identify whether a suspect model is a subsequent development of the victim model. To this end, we ... | Large Language Model, Fingerprint, Representation, Intellectual Property | null | 2,401 | 2410.14273 | [
-0.0399414487183094,
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0.... |
179 | Flat Reward in Policy Parameter Space Implies Robust Reinforcement Learning | https://openreview.net/forum?id=4OaO3GjP7k | [
"Hyun Kyu Lee",
"Sung Whan Yoon"
] | Oral | Investigating flat minima on loss surfaces in parameter space is well-documented in the supervised learning context, highlighting its advantages for model generalization. However, limited attention has been paid to the reinforcement learning (RL) context, where the impact of flatter reward landscapes in policy paramete... | Reinforcement learning, Flat Minima, Robust Reinforcement learning | null | 2,326 | null | [
-0.03053046204149723,
-0.01865505240857601,
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0.03982967510819435,
0.0359712615609169,
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0.0033391087781637907,
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180 | LLM-SR: Scientific Equation Discovery via Programming with Large Language Models | https://openreview.net/forum?id=m2nmp8P5in | [
"Parshin Shojaee",
"Kazem Meidani",
"Shashank Gupta",
"Amir Barati Farimani",
"Chandan K. Reddy"
] | Oral | Mathematical equations have been unreasonably effective in describing complex natural phenomena across various scientific disciplines. However, discovering such insightful equations from data presents significant challenges due to the necessity of navigating extremely large combinatorial hypothesis spaces. Current meth... | Symbolic Regression, Equation Discovery, Large Language Models, Evolutionary Search | We introduce LLM-SR, an approach that harnesses Large Language Models (LLMs) to discover governing equations from data in an efficient, knowledge-guided manner. | 2,272 | null | [
-0.03397534042596817,
-0.0035560117103159428,
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0.024288861081004143,
0.05451451241970062,
0.02923044003546238,
0.029769858345389366,
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0.011779905296862125,
0.03381120786070824,
-0.05553211271762848,
... |
181 | Backtracking Improves Generation Safety | https://openreview.net/forum?id=Bo62NeU6VF | [
"Yiming Zhang",
"Jianfeng Chi",
"Hailey Nguyen",
"Kartikeya Upasani",
"Daniel M. Bikel",
"Jason E Weston",
"Eric Michael Smith"
] | Oral | Text generation has a fundamental limitation almost by definition: there is no taking back tokens that have been generated, even when they are clearly problematic.
In the context of language model safety, when a partial unsafe generation is produced, language models by their nature tend to happily keep on generating si... | AI safety, Generation algorithm, Backtracking | We introduce a backtracking technique that trains language models to recover from unsafe generations and substantially improves generation safety. | 2,265 | 2409.14586 | [
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182 | Rethinking the generalization of drug target affinity prediction algorithms via similarity aware evaluation | https://openreview.net/forum?id=j7cyANIAxV | [
"Chenbin Zhang",
"Zhiqiang Hu",
"Jiang Chuchu",
"Wen Chen",
"JIE XU",
"Shaoting Zhang"
] | Oral | Drug-target binding affinity prediction is a fundamental task for drug discovery. It has been extensively explored in literature and promising results are reported. However, in this paper, we demonstrate that the results may be misleading and cannot be well generalized to real practice. The core observation is that the... | Drug-Target Affinity Prediction, Similarity-Aware Evaluation | null | 2,093 | null | [
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... |
183 | GridMix: Exploring Spatial Modulation for Neural Fields in PDE Modeling | https://openreview.net/forum?id=Fur0DtynPX | [
"Honghui Wang",
"Shiji Song",
"Gao Huang"
] | Oral | Significant advancements have been achieved in PDE modeling using neural fields. Despite their effectiveness, existing methods rely on global modulation, limiting their ability to reconstruct local details. While spatial modulation with vanilla grid-based representations offers a promising alternative, it struggles wit... | Partial Differential Equations, Neural Fields | null | 2,066 | null | [
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0.0... |
184 | Data Selection via Optimal Control for Language Models | https://openreview.net/forum?id=dhAL5fy8wS | [
"Yuxian Gu",
"Li Dong",
"Hongning Wang",
"Yaru Hao",
"Qingxiu Dong",
"Furu Wei",
"Minlie Huang"
] | Oral | This work investigates the selection of high-quality pre-training data from massive corpora to enhance LMs' capabilities for downstream usage.
We formulate data selection as a generalized Optimal Control problem, which can be solved theoretically by Pontryagin's Maximum Principle (PMP), yielding a set of necessary con... | Pre-training Language Models, Data Selection, Optimal Control | This paper introduces a framework to select high-quality pre-training data via optimal control. | 2,015 | 2410.07064 | [
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0.027467790991067886,
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185 | Simplifying, Stabilizing and Scaling Continuous-time Consistency Models | https://openreview.net/forum?id=LyJi5ugyJx | [
"Cheng Lu",
"Yang Song"
] | Oral | Consistency models (CMs) are a powerful class of diffusion-based generative models optimized for fast sampling. Most existing CMs are trained using discretized timesteps, which introduce additional hyperparameters and are prone to discretization errors. While continuous-time formulations can mitigate these issues, thei... | continuous-time consistency models, diffusion models, fast sampling | 2-step continuous-time consistency models reduce the gap to within 10\% in sample quality (FID) compared to best diffusion models | 1,982 | 2410.11081 | [
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0.01162... |
186 | Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping | https://openreview.net/forum?id=X1OfiRYCLn | [
"Yue Yang",
"Shuibo Zhang",
"Kaipeng Zhang",
"Yi Bin",
"Yu Wang",
"Ping Luo",
"Wenqi Shao"
] | Oral | Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across multimodal tasks such as visual perception and reasoning, leading to good performance on various multimodal evaluation benchmarks. However, these benchmarks keep a static nature and overlap with the pre-training data, resulting in fix... | Dynamic Evaluation, Vision-Language Bootstrapping, data contamination, Flexible Complexity, Large Vision-Language Model | We develop the first dynamic multimodal evaluation protocol with flexible complexity via Vision-Language Bootstrapping. | 1,837 | 2410.08695 | [
0.00867460947483778,
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0.04104915261268616,
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187 | Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models | https://openreview.net/forum?id=mtJSMcF3ek | [
"Yuda Song",
"Hanlin Zhang",
"Carson Eisenach",
"Sham M. Kakade",
"Dean Foster",
"Udaya Ghai"
] | Oral | Self-improvement is a mechanism in Large Language Model (LLM) pre-training, post-training and test-time inference. We explore a framework where the model verifies its own outputs, filters or reweights data based on this verification, and distills the filtered data. Despite several empirical successes, a fundamental un... | LLM, self-improvement, synthetic data, post-training, test-time optimization | We conduct a comprehensive examination on LLM self-improvement capability via the generation-verification gap. | 1,706 | 2412.02674 | [
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188 | SANA: Efficient High-Resolution Text-to-Image Synthesis with Linear Diffusion Transformers | https://openreview.net/forum?id=N8Oj1XhtYZ | [
"Enze Xie",
"Junsong Chen",
"Junyu Chen",
"Han Cai",
"Haotian Tang",
"Yujun Lin",
"Zhekai Zhang",
"Muyang Li",
"Ligeng Zhu",
"Yao Lu",
"Song Han"
] | Oral | We introduce Sana, a text-to-image framework that can efficiently generate images up to 4096$\times$4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU. Core designs include: (1) Deep compression autoencoder: unl... | Efficient AI, Diffusion Models, Text to Image generation | Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed. | 1,682 | null | [
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189 | Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning | https://openreview.net/forum?id=xoIeVdFO7U | [
"Chongyi Zheng",
"Jens Tuyls",
"Joanne Peng",
"Benjamin Eysenbach"
] | Oral | Self-supervised learning has the potential of lifting several of the key challenges in reinforcement learning today, such as exploration, representation learning, and reward design. Recent work (METRA) has effectively argued that moving away from mutual information and instead optimizing a certain Wasserstein distance ... | unsupervised learning, reinforcement learning, mutual information, successor feature | Through careful analysis of a prior method, we develop a new method called Contrastive Successor Features (CSF) that illustrates mutual information skill learning can be made highly effective. | 1,383 | 2412.08021 | [
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190 | When Selection Meets Intervention: Additional Complexities in Causal Discovery | https://openreview.net/forum?id=xByvdb3DCm | [
"Haoyue Dai",
"Ignavier Ng",
"Jianle Sun",
"Zeyu Tang",
"Gongxu Luo",
"Xinshuai Dong",
"Peter Spirtes",
"Kun Zhang"
] | Oral | We address the common yet often-overlooked selection bias in interventional studies, where subjects are selectively enrolled into experiments. For instance, participants in a drug trial are usually patients of the relevant disease; A/B tests on mobile applications target existing users only, and gene perturbation studi... | causal discovery, selection bias, experiments, interventions | null | 1,361 | 2503.07302 | [
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191 | LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias | https://openreview.net/forum?id=QQBPWtvtcn | [
"Haian Jin",
"Hanwen Jiang",
"Hao Tan",
"Kai Zhang",
"Sai Bi",
"Tianyuan Zhang",
"Fujun Luan",
"Noah Snavely",
"Zexiang Xu"
] | Oral | We propose the Large View Synthesis Model (LVSM), a novel transformer-based approach for scalable and generalizable novel view synthesis from sparse-view inputs. We introduce two architectures: (1) an encoder-decoder LVSM, which encodes input image tokens into a fixed number of 1D latent tokens, functioning as a fully ... | novel view synthesis, transformer, large model | We put forward a purely transformer-based large view synthesis model, which achieves impressive novel view synthesis results on both object-level and scene-level with minimal 3D inductive bias. | 1,355 | 2410.17242 | [
0.016413509845733643,
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192 | Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective | https://openreview.net/forum?id=tcvMzR2NrP | [
"Neta Shaul",
"Itai Gat",
"Marton Havasi",
"Daniel Severo",
"Anuroop Sriram",
"Peter Holderrieth",
"Brian Karrer",
"Yaron Lipman",
"Ricky T. Q. Chen"
] | Oral | The design space of discrete-space diffusion or flow generative models are significantly less well-understood than their continuous-space counterparts, with many works focusing only on a simple masked construction.
In this work, we aim to take a holistic approach to the construction of discrete generative models based ... | flow matching, discrete generative modeling | Through the lens of kinetic optimality, we expand the design space of Discrete Flow Matching, allowing the use of any probability path and simultaneously justifying existing mixture paths. | 1,351 | 2412.03487 | [
0.010729641653597355,
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-0.0... |
193 | Cut Your Losses in Large-Vocabulary Language Models | https://openreview.net/forum?id=E4Fk3YuG56 | [
"Erik Wijmans",
"Brody Huval",
"Alexander Hertzberg",
"Vladlen Koltun",
"Philipp Kraehenbuehl"
] | Oral | As language models grow ever larger, so do their vocabularies.
This has shifted the memory footprint of LLMs during training disproportionately to one single layer: the cross-entropy in the loss computation.
Cross-entropy builds up a logit matrix with entries for each pair of input tokens and vocabulary items and, for ... | large language model, large vocabulary, efficient | We propose Cut Cross-Entropy (CCE), a method that computes the cross-entropy loss with negligible memory consumption. | 1,344 | 2411.09009 | [
-0.03541720286011696,
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194 | AFlow: Automating Agentic Workflow Generation | https://openreview.net/forum?id=z5uVAKwmjf | [
"Jiayi Zhang",
"Jinyu Xiang",
"Zhaoyang Yu",
"Fengwei Teng",
"Xiong-Hui Chen",
"Jiaqi Chen",
"Mingchen Zhuge",
"Xin Cheng",
"Sirui Hong",
"Jinlin Wang",
"Bingnan Zheng",
"Bang Liu",
"Yuyu Luo",
"Chenglin Wu"
] | Oral | Large language models (LLMs) have demonstrated remarkable potential in solving complex tasks across diverse domains, typically by employing agentic workflows that follow detailed instructions and operational sequences. However, constructing these workflows requires significant human effort, limiting scalability and gen... | LLM Agent; Prompt Optimization; Workflow Generation | We introduce the field of Agentic Workflow Optimization and propose an effective search algorithm called AFLOW, enabling it to surpass manually constructed workflows on six reasoning datasets. | 1,308 | 2410.10762 | [
0.01045062392950058,
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-0.... |
195 | Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models | https://openreview.net/forum?id=uAFHCZRmXk | [
"Simon Schrodi",
"David T. Hoffmann",
"Max Argus",
"Volker Fischer",
"Thomas Brox"
] | Oral | Contrastive vision-language models (VLMs), like CLIP, have gained popularity for their versatile applicability to various downstream tasks. Despite their successes in some tasks, like zero-shot object recognition, they perform surprisingly poor on other tasks, like attribute recognition. Previous work has attributed th... | CLIP, modality gap, object bias, contrastive loss, data-centric, vision language models, VLM | We find that an information imbalance between images and texts leads to the modality gap and object bias of contrastive VLMs. We study both phenomena in depth, eliminate common misconceptions, and improve the understanding of both of them. | 1,079 | 2404.07983 | [
0.016726678237318993,
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0.012613823637366295,
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0.06355578452348709,
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0.038081955164670944,
-0.08204766362905502,
-0.0... |
196 | FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference | https://openreview.net/forum?id=OfjIlbelrT | [
"Xunhao Lai",
"Jianqiao Lu",
"Yao Luo",
"Yiyuan Ma",
"Xun Zhou"
] | Oral | Large language models (LLMs) encounter computational challenges during long-sequence inference, especially in the attention pre-filling phase, where the complexity grows quadratically with the prompt length. Previous efforts to mitigate these challenges have relied on fixed sparse attention patterns or identifying spar... | Large Language Models (LLMs), LLM inference, Long-context LLMs, Sparse Attention Mechanism | FlexPrefill is a novel sparse attention mechanism for large language models that dynamically adapts attention patterns and computational budgets in real-time to optimize performance for each input and attention head. | 1,022 | 2502.20766 | [
0.010869160294532776,
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0.03114846721291542,
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-0.006... |
197 | REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments | https://openreview.net/forum?id=NxyfSW6mLK | [
"Kaustubh Sridhar",
"Souradeep Dutta",
"Dinesh Jayaraman",
"Insup Lee"
] | Oral | Building generalist agents that can rapidly adapt to new environments is a key challenge for deploying AI in the digital and real worlds. Is scaling current agent architectures the most effective way to build generalist agents? We propose a novel approach to pre-train relatively small policies on relatively small datas... | Generalist Agent, Retrieval, In-Context Learning, VLA, Imitation Learning, Reinforcement Learning | We propose a retrieval-augmented generalist agent that can adapt to new environments via in-context learning | 961 | 2412.04759 | [
-0.0340435765683651,
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0.04562932625412941,
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0.016218138858675957,
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0.03398275747895241,
-0.07733441144227982,
-... |
198 | MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models | https://openreview.net/forum?id=HnhNRrLPwm | [
"Peng Xia",
"Siwei Han",
"Shi Qiu",
"Yiyang Zhou",
"Zhaoyang Wang",
"Wenhao Zheng",
"Zhaorun Chen",
"Chenhang Cui",
"Mingyu Ding",
"Linjie Li",
"Lijuan Wang",
"Huaxiu Yao"
] | Oral | Interleaved multimodal comprehension and generation, enabling models to produce and interpret both images and text in arbitrary sequences, have become a pivotal area in multimodal learning. Despite significant advancements, the evaluation of this capability remains insufficient. Existing benchmarks suffer from limitati... | large vision-language model, interleaved text-and-image evaluation | null | 944 | 2410.10139 | [
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0.0032563682179898024,
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0.04888742044568062,
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199 | Do as We Do, Not as You Think: the Conformity of Large Language Models | https://openreview.net/forum?id=st77ShxP1K | [
"Zhiyuan Weng",
"Guikun Chen",
"Wenguan Wang"
] | Oral | Recent advancements in large language models (LLMs) revolutionize the field of intelligent agents, enabling collaborative multi-agent systems capable of tackling complex problems across various domains. However, the potential of conformity within these systems, analogous to phenomena like conformity bias and group-thin... | Large Language Models, Conformity, Multi-agent System | null | 934 | 2501.13381 | [
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-0.03062272258102894,
0.021455347537994385,
-0.06998851895332336,
-0... |
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