Bridging scales: At the crossroads among renormalisation group, multi-scale modelling, and deep learning
Aula Renzo Leonardi
ECT*
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Machine learning will define the 21st Century: from simple image classification to text generation and decision making, its impact on society will be nothing but immense. At present, the detailed mechanisms behind the power of AI still evade our understanding; growing evidence, however, suggests that it is possible to rationalise how deep learning works in terms that are very familiar to theoretical physicists, that is, the renormalisation group. The systematic, hierarchical coarsening of detailed information into increasingly simpler and more collective features is a cornerstone of modern physics, and it can be leveraged not only to make sense of machine learning’s baffling
capabilities, but also and most importantly to steer its development. This workshop will explore the area where theoretical physics of soft and condensed matter and deep learning overlap, looking for novel and more powerful tools to model, investigate, and understand the world around us.
Alessandro Ingrosso
Alessandro Roggero
Alessio Lugnan
Andrea Pedrielli
Andreas Ipp
Antonio Trovato
Arnau Rios Huguet
Beatriz Seoane Bartolome
Christine Peter
Danial Ghamari
Di Luo
Emanuele Locatelli
Fabrizio Napolitano
Federica Gerace
Federico Grasselli
Francesco Pederiva
Guglielmo Grillo
jean barbier
Kai Zhou
Luca Tubiana
Margherita Mele
Matteo Scandola
Mattia Scandolo
Oriel Kiss
Pietro Faccioli
Pratyush Tiwary
Raffaello Potestio
Roberto Menichetti
Sanghamitra Neogi
Susana Marin Aguilar
Tristan Bereau
will noid
Yannick Meurice