Papers
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.
UPSCALE: Unconstrained Channel Pruning
Alvin Wan, Hanxiang Hao, Kaushik Patnaik et al.
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems
Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson et al.
User-level Private Stochastic Convex Optimization with Optimal Rates
Raef Bassily, Ziteng Sun
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies
Gati V Aher, Rosa I. Arriaga, Adam Tauman Kalai
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy
Xing Liu, Andrew B. Duncan, Axel Gandy
VA-learning as a more efficient alternative to Q-learning
Yunhao Tang, Remi Munos, Mark Rowland et al.
Variance Control for Distributional Reinforcement Learning
Qi Kuang, Zhoufan Zhu, Liwen Zhang et al.
Variational Autoencoding Neural Operators
Jacob H Seidman, Georgios Kissas, George J. Pappas et al.
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills
Seongun Kim, Kyowoon Lee, Jaesik Choi
Variational Mixture of HyperGenerators for Learning Distributions over Functions
Batuhan Koyuncu, Pablo Sanchez Martin, Ignacio Peis et al.