🏢 Kyoto University
Parameter-free Clipped Gradient Descent Meets Polyak
·1918 words·10 mins·
loading
·
loading
Machine Learning
Optimization
🏢 Kyoto University
Parameter-free optimization is revolutionized! Inexact Polyak Stepsize achieves the same convergence rate as clipped gradient descent but without any hyperparameter tuning, saving time and computatio…
Enhancing Chess Reinforcement Learning with Graph Representation
·2930 words·14 mins·
loading
·
loading
AI Generated
Machine Learning
Reinforcement Learning
🏢 Kyoto University
AlphaGateau: a novel Graph Neural Network architecture outperforms previous chess AI models by leveraging graph representations for faster training and superior generalization to different board sizes…