Machine Learning and the Renormalization Group

Aula Renzo Leonardi - Villa Tambosi

Str. delle Tabarelle, 286, 38123 Villazzano TN

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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 als 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.


  • Julian Urban (MIT)
  • Daniel Hackett (Fermilab)
  • Anna Hasenfratz (University of Colorado Boulder)
  • Jan Pawlowski (Heidelberg University)
  • Biagio Lucini (Swansea University)
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