Papers
11,951 papers found
What should a neuron aim for? Designing local objective functions based on information theory
Andreas Christian Schneider, Valentin Neuhaus, David Alexander Ehrlich et al.
What's New in My Data? Novelty Exploration via Contrastive Generation
Masaru Isonuma, Ivan Titov
What's the Move? Hybrid Imitation Learning via Salient Points
Priya Sundaresan, Hengyuan Hu, Quan Vuong et al.
What to align in multimodal contrastive learning?
Benoit Dufumier, Javiera Castillo Navarro, Devis Tuia et al.
When Attention Sink Emerges in Language Models: An Empirical View
Xiangming Gu, Tianyu Pang, Chao Du et al.
When does compositional structure yield compositional generalization? A kernel theory.
Samuel Lippl, Kim Stachenfeld
When do GFlowNets learn the right distribution?
Tiago Silva, Rodrigo Barreto Alves, Eliezer de Souza da Silva et al.
When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach
Qian Chen, Lei Li, Qian Li et al.
When Graph Neural Networks Meet Dynamic Mode Decomposition
Dai Shi, Lequan Lin, Andi Han et al.
When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers
Hongkang Li, Yihua Zhang, Shuai Zhang et al.
When LLMs Play the Telephone Game: Cultural Attractors as Conceptual Tools to Evaluate LLMs in Multi-turn Settings
Jérémy Perez, Grgur Kovač, Corentin Léger et al.
When narrower is better: the narrow width limit of Bayesian parallel branching neural networks
Zechen Zhang, Haim Sompolinsky
When Prompt Engineering Meets Software Engineering: CNL-P as Natural and Robust "APIs'' for Human-AI Interaction
Zhenchang Xing, Yang Liu, Zhuo Cheng et al.
When Selection Meets Intervention: Additional Complexities in Causal Discovery
Haoyue Dai, Ignavier Ng, Jianle Sun et al.
Where Am I and What Will I See: An Auto-Regressive Model for Spatial Localization and View Prediction
Junyi Chen, Di Huang, Weicai Ye et al.
Which Tasks Should Be Compressed Together? A Causal Discovery Approach for Efficient Multi-Task Representation Compression
Sha Guo, Jing Chen, Zixuan Hu et al.
Why Does the Effective Context Length of LLMs Fall Short?
Chenxin An, Jun Zhang, Ming Zhong et al.
Why In-Context Learning Models are Good Few-Shot Learners?
Shiguang Wu, Yaqing Wang, Quanming Yao
Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks
Nguyen Hung-Quang, Ngoc-Hieu Nguyen, The-Anh Ta et al.
Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse
Arthur Jacot, Peter Súkeník, Zihan Wang et al.
WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild
Bill Yuchen Lin, Yuntian Deng, Khyathi Chandu et al.
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Haipeng Luo, Qingfeng Sun, Can Xu et al.
Words in Motion: Extracting Interpretable Control Vectors for Motion Transformers
Omer Sahin Tas, Royden Wagner
WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models
Shengda Fan, Xin Cong, Yuepeng Fu et al.
World Model on Million-Length Video And Language With Blockwise RingAttention
Hao Liu, Wilson Yan, Matei Zaharia et al.