All Papers

What Effects the Generalization in Visual Reinforcement Learning: Policy Consistency with Truncated Return Prediction

What How and When Should Object Detectors Update in Continually Changing Test Domains?

Jayeon Yoo, Dongkwan Lee, Inseop Chung et al.

What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models

Letian Zhang, Xiaotong Zhai, Zhongkai Zhao et al.

What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding

Hongkang Li, Meng Wang, Tengfei Ma et al.

What is Dataset Distillation Learning?

William Yang, Ye Zhu, Zhiwei Deng et al.

What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis

Waïss Azizian, Franck Iutzeler, Jérôme Malick et al.

What Makes a Good Prune? Maximal Unstructured Pruning for Maximal Cosine Similarity

Gabryel Mason-Williams, Fredrik Dahlqvist

What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perception

Wanfang Su, Lixing Chen, Yang Bai et al.

What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Wei Liu, Weihao Zeng, Keqing He et al.

What Makes Quantization for Large Language Model Hard? An Empirical Study from the Lens of Perturbation

Huankang Guan, Rynson W.H. Lau

What Matters to You? Towards Visual Representation Alignment for Robot Learning

Thomas Tian, Chenfeng Xu, Masayoshi Tomizuka et al.

What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation

Aaditya Singh, Ted Moskovitz, Feilx Hill et al.

What's in a Prior? Learned Proximal Networks for Inverse Problems

Zhenghan Fang, Sam Buchanan, Jeremias Sulam

What's In My Big Data?

Yanai Elazar, Akshita Bhagia, Ian Magnusson et al.

What Sketch Explainability Really Means for Downstream Tasks?

Hmrishav Bandyopadhyay, Pinaki Nath Chowdhury, Ayan Kumar Bhunia et al.

What’s the score? Automated Denoising Score Matching for Nonlinear Diffusions

raghav singhal, Mark Goldstein, Rajesh Ranganath

What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection

XiaoHui Zhang, Jiangyan Yi, Chenglong Wang et al.

What When and Where? Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions

Brian Chen, Nina Shvetsova, Andrew Rouditchenko et al.

What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement

Xisen Jin, Xiang Ren

What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks

Ching-Yun (Irene) Ko, Pin-Yu Chen, Payel Das et al.

What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs

Alex Trevithick, Matthew Chan, Towaki Takikawa et al.

When and How Does In-Distribution Label Help Out-of-Distribution Detection?

Xuefeng Du, Yiyou Sun, Sharon Li

When and How do negative prompts take effect?

Yuanhao Ban, Ruochen Wang, Tianyi Zhou et al.

When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-Making

Kate Donahue, Sreenivas Gollapudi, Kostas Kollias

When can transformers reason with abstract symbols?

Enric Boix-Adserà, Omid Saremi, Emmanuel Abbe et al.

When CEGAR Meets Regression: A Love Story in Optimal Classical Planning

Martín Pozo, Alvaro Torralba, Carlos Linares Lopez

When Do Program-of-Thought Works for Reasoning?

Zhen Bi, Ningyu Zhang, Yinuo Jiang et al.

When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations

Aleksandar Petrov, Philip Torr, Adel Bibi

When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions

Zhening Li, Gabriel Poesia, Armando Solar-Lezama

When Do We Not Need Larger Vision Models?

Baifeng Shi, Ziyang Wu, Maolin Mao et al.

When Fast Fourier Transform Meets Transformer for Image Restoration

xingyu jiang, Xiuhui Zhang, Ning Gao et al.

When is Transfer Learning Possible?

My Phan, Kianté Brantley, Stephanie Milani et al.

When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models

Haoran You, Yichao Fu, Zheng Wang et al.

When Model Meets New Normals: Test-Time Adaptation for Unsupervised Time-Series Anomaly Detection

When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset

Yi Zhang, Wang Zeng, Sheng Jin et al.

When Representations Align: Universality in Representation Learning Dynamics

Loek van Rossem, Andrew Saxe

When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method

Biao Zhang, Zhongtao Liu, Colin Cherry et al.

When Semantic Segmentation Meets Frequency Aliasing

Linwei Chen, Lin Gu, Ying Fu

When should we prefer Decision Transformers for Offline Reinforcement Learning?

Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard et al.

When StyleGAN Meets Stable Diffusion: a W+ Adapter for Personalized Image Generation

Xiaoming Li, Xinyu Hou, Chen Change Loy

When to Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks

Haihang Wu, Wei Wang, Tamasha Malepathirana et al.

When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming

Hussein Mozannar, Gagan Bansal, Adam Fourney et al.

When Visual Grounding Meets Gigapixel-level Large-scale Scenes: Benchmark and Approach

TAO MA, Bing Bai, Haozhe Lin et al.

When Will Gradient Regularization Be Harmful?

Yang Zhao, Hao Zhang, Xiuyuan Hu

Where am I? Scene Retrieval with Language

Jiaqi Chen, Daniel Barath, Iro Armeni et al.

Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual

Ruichu Cai, Yuxuan Zhu, Jie Qiao et al.

Where We Have Arrived in Proving the Emergence of Sparse Interaction Primitives in DNNs

Qihan Ren, Jiayang Gao, Wen Shen et al.

Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning

Yuxiao Wen, Arthur Jacot

Which Is More Effective in Label Noise Cleaning, Correction or Filtering?

Gaoxia Jiang, Jia Zhang, Xuefei Bai et al.

Which Model Generated This Image? A Model-Agnostic Approach for Origin Attribution

Fengyuan Liu, Haochen Luo, Yiming Li et al.