Papers
Modeling Structure with Undirected Neural Networks
Tsvetomila Mihaylova, Vlad Niculae, Andre Martins
Model Selection in Batch Policy Optimization
Jonathan Lee, George Tucker, Ofir Nachum et al.
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre et al.
Model-Value Inconsistency as a Signal for Epistemic Uncertainty
Angelos Filos, Eszter Vértes, Zita Marinho et al.
ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias
Yupu Lu, Shijie Lin, Guanqi Chen et al.
Modular Conformal Calibration
Charles Marx, Shengjia Zhao, Willie Neiswanger et al.
Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks
Zhaoning Yu, Hongyang Gao
Monarch: Expressive Structured Matrices for Efficient and Accurate Training
Tri Dao, Beidi Chen, Nimit S Sohoni et al.
More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize
Alexander Wei, Wei Hu, Jacob Steinhardt
Multiclass learning with margin: exponential rates with no bias-variance trade-off
Stefano Vigogna, Giacomo Meanti, Ernesto De Vito et al.
Multicoated Supermasks Enhance Hidden Networks
Yasuyuki Okoshi, Ángel López Garcı́a-Arias, Kazutoshi Hirose et al.
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
Yan Zeng, Xinsong Zhang, Hang Li
Multi-Level Branched Regularization for Federated Learning
Jinkyu Kim, Geeho Kim, Bohyung Han
Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms
Xuchuang Wang, Hong Xie, John C. S. Lui
Multirate Training of Neural Networks
Tiffany J Vlaar, Benedict Leimkuhler
Multi Resolution Analysis (MRA) for Approximate Self-Attention
Zhanpeng Zeng, Sourav Pal, Jeffery Kline et al.
Multi-scale Feature Learning Dynamics: Insights for Double Descent
Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio et al.
Multi-slots Online Matching with High Entropy
Xingyu Lu, Qintong Wu, Wenliang Zhong
Multi-Task Learning as a Bargaining Game
Aviv Navon, Aviv Shamsian, Idan Achituve et al.
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
Wentao Zhang, Zeang Sheng, Mingyu Yang et al.
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
Martin Genzel, Ingo Gühring, Jan Macdonald et al.
Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
Pihe Hu, Yu Chen, Longbo Huang
Nearly Optimal Catoni’s M-estimator for Infinite Variance
Sujay Bhatt, Guanhua Fang, Ping Li et al.
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee
Tianhao Wu, Yunchang Yang, Han Zhong et al.