Papers
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.
Understanding the Role of Feedback in Online Learning with Switching Costs
Duo Cheng, Xingyu Zhou, Bo Ji
Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data
Ali Siahkoohi, Rudy Morel, Maarten V. De Hoop et al.
Unifying Molecular and Textual Representations via Multi-task Language Modelling
Dimitrios Christofidellis, Giorgio Giannone, Jannis Born et al.
Unifying Nesterov’s Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions
Jungbin Kim, Insoon Yang
Unit Scaling: Out-of-the-Box Low-Precision Training
Charlie Blake, Douglas Orr, Carlo Luschi
Universal Morphology Control via Contextual Modulation
Zheng Xiong, Jacob Beck, Shimon Whiteson
Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data
Lena Podina, Brydon Eastman, Mohammad Kohandel
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability
Jianing Zhu, Hengzhuang Li, Jiangchao Yao et al.
Unlocking Slot Attention by Changing Optimal Transport Costs
Yan Zhang, David W. Zhang, Simon Lacoste-Julien et al.
Unscented Autoencoder
Faris Janjos, Lars Rosenbaum, Maxim Dolgov et al.
Unsupervised Out-of-Distribution Detection with Diffusion Inpainting
Zhenzhen Liu, Jin Peng Zhou, Yufan Wang et al.
Unsupervised Skill Discovery for Learning Shared Structures across Changing Environments
Sang-Hyun Lee, Seung-Woo Seo
Unveiling the Latent Space Geometry of Push-Forward Generative Models
Thibaut Issenhuth, Ugo Tanielian, Jeremie Mary et al.
Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features
Chieh Hubert Lin, Hung-Yu Tseng, Hsin-Ying Lee et al.
UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers
Dachuan Shi, Chaofan Tao, Ying Jin et al.