Papers
Tuning Computer Vision Models With Task Rewards
André Susano Pinto, Alexander Kolesnikov, Yuge Shi et al.
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
Yu Meng, Martin Michalski, Jiaxin Huang et al.
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy
Blake Woodworth, Konstantin Mishchenko, Francis Bach
UMD: Unsupervised Model Detection for X2X Backdoor Attacks
Zhen Xiang, Zidi Xiong, Bo Li
Uncertain Evidence in Probabilistic Models and Stochastic Simulators
Andreas Munk, Alexander Mead, Frank Wood
Uncertainty Estimation by Fisher Information-based Evidential Deep Learning
Danruo Deng, Guangyong Chen, Yang Yu et al.
Uncertainty Estimation for Molecules: Desiderata and Methods
Tom Wollschläger, Nicholas Gao, Bertrand Charpentier et al.
Unconstrained Online Learning with Unbounded Losses
Andrew Jacobsen, Ashok Cutkosky
Uncovering Adversarial Risks of Test-Time Adaptation
Tong Wu, Feiran Jia, Xiangyu Qi et al.
Under-Counted Tensor Completion with Neural Incorporation of Attributes
Shahana Ibrahim, Xiao Fu, Rebecca Hutchinson et al.
Understand and Modularize Generator Optimization in ELECTRA-style Pretraining
Chengyu Dong, Liyuan Liu, Hao Cheng et al.
Understanding and Defending Patched-based Adversarial Attacks for Vision Transformer
Liang Liu, Yanan Guo, Youtao Zhang et al.
Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective
Liangliang Shi, Gu Zhang, Haoyu Zhen et al.
Understanding Backdoor Attacks through the Adaptability Hypothesis
Xun Xian, Ganghua Wang, Jayanth Srinivasa et al.
Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias
Ryo Karakida, Tomoumi Takase, Tomohiro Hayase et al.
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing
Jikai Jin, Zhiyuan Li, Kaifeng Lyu et al.
Understanding Int4 Quantization for Language Models: Latency Speedup, Composability, and Failure Cases
Xiaoxia Wu, Cheng Li, Reza Yazdani Aminabadi et al.
Understanding Oversquashing in GNNs through the Lens of Effective Resistance
Mitchell Black, Zhengchao Wan, Amir Nayyeri et al.
Understanding Plasticity in Neural Networks
Clare Lyle, Zeyu Zheng, Evgenii Nikishin et al.
Understanding Self-Distillation in the Presence of Label Noise
Rudrajit Das, Sujay Sanghavi
Understanding Self-Predictive Learning for Reinforcement Learning
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond et al.
Understanding the Complexity Gains of Single-Task RL with a Curriculum
Qiyang Li, Yuexiang Zhai, Yi Ma et al.
Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits
Xuejie Liu, Anji Liu, Guy Van Den Broeck et al.
Understanding the Impact of Adversarial Robustness on Accuracy Disparity
Yuzheng Hu, Fan Wu, Hongyang Zhang et al.