Progress and hurdles in the statistical mechanics of deep learning

Abstract: Understanding the great performances of deep neural networks is a very active direction of research with contributions coming from a wide variety of fields. The statistical mechanics of learning is a theoretical framework dating back to the 80s studying learning problems from a physicist viewpoint and using tools from the physics of disordered systems. In this talk, I will first go over this traditional framework, which relies on the teacher-student scenario, bayesian analysis and mean-field approximations. Then I will discuss some recent advances in the corresponding analysis of modern deep neural network, and highlight remaining challenges.

data structures and algorithmsmachine learningmathematical physicsinformation theoryoptimization and controldata analysis, statistics and probability

Audience: researchers in the topic

( video )

Mathematics, Physics and Machine Learning (IST, Lisbon)

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Zoom link: videoconf-colibri.zoom.us/j/91599759679