Summary of Why You Don’t Overfit, and Don’t Need Bayes If You Only Train For One Epoch, by Laurence Aitchison
Why you don’t overfit, and don’t need Bayes if you only train for one epochby…
Why you don’t overfit, and don’t need Bayes if you only train for one epochby…
A Neural Network Training Method Based on Distributed PID Controlby Jiang KunFirst submitted to arxiv…
Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeableby Bao NguyenFirst submitted to…
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Material synthesis through simulations guided by machine learning: a position paperby Usman Syed, Federico Cunico,…
Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inferenceby Yunhui Liu, Xinyi…
Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamicsby Stefan Schestakov, Simon GottschalkFirst…
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecastingby Qingxiang Liu, Sheng Sun, Yuxuan Liang,…
Out-Of-Distribution Detection with Diversification (Provably)by Haiyun Yao, Zongbo Han, Huazhu Fu, Xi Peng, Qinghua Hu,…
MMGenBench: Fully Automatically Evaluating LMMs from the Text-to-Image Generation Perspectiveby Hailang Huang, Yong Wang, Zixuan…