Papers
UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units
Igor Fedorov, Ramon Matas, Hokchhay Tann et al.
u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality
Wei-Ning Hsu, Bowen Shi
ULNeF: Untangled Layered Neural Fields for Mix-and-Match Virtual Try-On
Igor Santesteban, Miguel Otaduy, Nils Thuerey et al.
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
Zongbo Han, Zhipeng Liang, Fan Yang et al.
Uncalibrated Models Can Improve Human-AI Collaboration
Kailas Vodrahalli, Tobias Gerstenberg, James Y Zou
Uncertainty-Aware Hierarchical Refinement for Incremental Implicitly-Refined Classification
Jian Yang, Kai Zhu, Kecheng Zheng et al.
Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game
Guiliang Liu, Yudong Luo, Oliver Schulte et al.
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture
Myong Chol Jung, He Zhao, Joanna Dipnall et al.
Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning
Guy Tennenholtz, Shie Mannor
Uncoupled Learning Dynamics with $O(\log T)$ Swap Regret in Multiplayer Games
Ioannis Anagnostides, Gabriele Farina, Christian Kroer et al.
Uncovering the Structural Fairness in Graph Contrastive Learning
Ruijia Wang, Xiao Wang, Chuan Shi et al.
Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment
Daniel Vera Nieto, Luigi Celona, Clara Fernandez Labrador
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
Fabrizio Frasca, Beatrice Bevilacqua, Michael Bronstein et al.
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation
Yao Qin, Chiyuan Zhang, Ting Chen et al.
Understanding Benign Overfitting in Gradient-Based Meta Learning
Lisha Chen, Songtao Lu, Tianyi Chen
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty
Jaehoon Oh, Sungnyun Kim, Namgyu Ho et al.
Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration
Fanghui Liu, Luca Viano, Volkan Cevher
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning
Tao Yu, Yichi Zhang, Zhiru Zhang et al.
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective
Rongzhe Wei, Haoteng YIN, Junteng Jia et al.
Understanding Programmatic Weak Supervision via Source-aware Influence Function
Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh et al.
Understanding Robust Learning through the Lens of Representation Similarities
Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag et al.
Understanding Square Loss in Training Overparametrized Neural Network Classifiers
Tianyang Hu, Jun WANG, Wenjia Wang et al.
Understanding the Eluder Dimension
Gene Li, Pritish Kamath, Dylan J Foster et al.
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning
Setareh Cohan, Nam Hee Kim, David Rolnick et al.