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stat.ML2023★ 2 cited
A General Framework for Equivariant Neural Networks on Reductive Lie Groups
Ilyes Batatia, Mario Geiger, Jose Munoz +3
Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, or the unitary groups, play essential roles across scientific fields as diverse as high energy physics, quan…
stat.ML2019★ 38 cited
Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm
Stefano Spigler, Mario Geiger, Matthieu Wyart
How many training data are needed to learn a supervised task? It is often observed that the generalization error decreases as where is the number of training examples…
stat.ML2018★ 47 cited
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
M. Baity-Jesi, L. Sagun, M. Geiger +6
We analyze numerically the training dynamics of deep neural networks (DNN) by using methods developed in statistical physics of glassy systems. The two main issues we address are (…