Papers
11,955 papers found
Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders
Huangjie Zheng, Pengcheng He, Weizhu Chen et al.
Truthful Self-Play
Shohei Ohsawa
TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation
Hyesu Lim, Byeonggeun Kim, Jaegul Choo et al.
Tuning Frequency Bias in Neural Network Training with Nonuniform Data
Annan Yu, Yunan Yang, Alex Townsend
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection
Shuyang Yu, Junyuan Hong, Haotao Wang et al.
TVSPrune - Pruning Non-discriminative filters via Total Variation separability of intermediate representations without fine tuning
Chaitanya Murti, Tanay Narshana, Chiranjib Bhattacharyya
TypeT5: Seq2seq Type Inference using Static Analysis
Jiayi Wei, Greg Durrett, Isil Dillig
UL2: Unifying Language Learning Paradigms
Yi Tay, Mostafa Dehghani, Vinh Q. Tran et al.
Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States
Mingjie Li, Yifei Wang, Yisen Wang et al.
Unbiased Supervised Contrastive Learning
Carlo Alberto Barbano, Benoit Dufumier, Enzo Tartaglione et al.
Uncertainty-Aware Alignment Network for Cross-Domain Video-Text Retrieval
Xiaoshuai Hao, Wanqian Zhang
Understanding and Adopting Rational Behavior by Bellman Score Estimation
Kuno Kim, Stefano Ermon
Understanding DDPM Latent Codes Through Optimal Transport
Valentin Khrulkov, Gleb Ryzhakov, Andrei Chertkov et al.
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example
Xingyu Zhu, Zixuan Wang, Xiang Wang et al.
Understanding Embodied Reference with Touch-Line Transformer
Yang Li, Xiaoxue Chen, Hao Zhao et al.
Understanding Influence Functions and Datamodels via Harmonic Analysis
Nikunj Saunshi, Arushi Gupta, Mark Braverman et al.
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles
Martin Bjerke, Lukas Schott, Kristopher T Jensen et al.
Understanding new tasks through the lens of training data via exponential tilting
Subha Maity, Mikhail Yurochkin, Moulinath Banerjee et al.
Understanding the Covariance Structure of Convolutional Filters
Asher Trockman, Devin Willmott, J Zico Kolter
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization
Difan Zou, Yuan Cao, Yuanzhi Li et al.
Understanding The Robustness of Self-supervised Learning Through Topic Modeling
Zeping Luo, Shiyou Wu, Cindy Weng et al.
Understanding Train-Validation Split in Meta-Learning with Neural Networks
Xinzhe Zuo, Zixiang Chen, Huaxiu Yao et al.
Understanding weight-magnitude hyperparameters in training binary networks
Joris Quist, Yunqiang Li, Jan van Gemert
Understanding Why Generalized Reweighting Does Not Improve Over ERM
Runtian Zhai, Chen Dan, J Zico Kolter et al.