Giulio Biroli - École normale supérieure (Paris)
Wednesday, November 23, 10:45AM (Zoom meeting starts at 10:30)
"Renormalization Group Theory and Machine Learning"
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Reconstructing, or generating, high dimensional distributions starting from data is a central problem in machine lea tion Group — that combines ideas from physics (renormalization group theory) and computer science (wavelets, stable representations of operators). The Wavelet Conditional Renormalization Group allows to reconstruct in a very efficient rning and data sciences. I will present a method — The Wavelet Conditional Renormaliza way classes of high dimensional distributions hierarchically from large to small spatial scales. I will present the method and then show its applications to data from statistical physics and cosmology. The Wavelet Conditional Renormalization Group Method also provides interesting insights on the interplay between structures of data and architectures of deep neural networks.