Machine Learning and the Renormalization Group
Aula Renzo Leonardi
ECT*
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From stochastic annealing to diffusion models, the unreasonable effectiveness of physics concepts for the design of powerful machine learning algorithms has become increasingly apparent over the past two decades. Likewise, similarities between renormalization group transformations and neural networks are being explored for various applications, ranging from hierarchical models in computer vision to trivializing maps in lattice field theory. On the other hand, there has also been growing interest in the utilization of information bottleneck and quantum field theory techniques towards an improved theoretical understanding of the empirical successes of deep learning. Furthermore, exciting mathematical connections between functional renormalization group equations and optimal transport theory are being understood for the first time. This interdisciplinary workshop aims to provide an interface for experts from different fields sharing a common interest in this topic, with the goal of advancing our collective understanding and identifying promising directions for future work.
Anindita Maiti
Antonino Marciano
Arif Sarkar
Biagio Lucini
Chan Ju Park
Daniel Hackett
Denis Boyda
Diaa Eddin Habibi
Dimitrios Bachtis
Fernando Romero-Lopez
Friederike Ihssen
Gert Aarts
Gurtej Kanwar
Harold Erbin
Jacob Finkenrath
Jan M. Pawlowski
Jascha Sohl-Dickstein
Jim Halverson
Julian Urban
Kai Zhou
Kieran Holland
Lingxiao Wang
Maciej Koch-Janusz
Marc Bauer
Mathis Gerdes
Matteo Favoni
Miranda Cheng
Misaki Ozawa
Márcio Ferreira
Nicolo Defenu
Ouraman Hajizadeh
Raffaello Potestio
Renzo Kapust
Ro Jefferson
Sabine Andergassen
Samuele Pedrielli
Semen Rezchikov
Sho Yaida
Shradha Ramakrishnan
Urs Wenger
Vincent Lahoche