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Information theory for data-driven model reduction in physics and biology
Model reduction is the construction of simple yet predictive descriptions of the dynamics of many-body systems in terms of a few relevant variables.
Training Deep Neural Networks by optimizing over nonlocal paths in hyperparameter space
Hyperparameter optimization is both a practical issue and an interesting theoretical problem in training of deep architectures.
Mutual Information, Neural Networks and the Renormalization Group
Physical systems differring in their microscopic details often display strikingly similar behaviour when probed at macroscopic scales.
Disordered Systems and Neural Networks Statistical Mechanics Information Theory Machine Learning Information Theory Machine Learning
