1.9k citations · 3.4k across the 26 of their papers we have counts for
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Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training
Mario Geiger, Leonardo Petrini, Matthieu Wyart
Deep learning algorithms are responsible for a technological revolution in a variety of tasks including image recognition or Go playing. Yet, why they work is not understood. Ultim…
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties
Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt +1
Equivariant neural networks (ENNs) are graph neural networks embedded in and are well suited for predicting molecular properties. The ENN library e3nn has customizab…
Geometric compression of invariant manifolds in neural nets
Jonas Paccolat, Leonardo Petrini, Mario Geiger +2
We study how neural networks compress uninformative input space in models where data lie in dimensions, but whose label only vary within a linear manifold of dimension $d_\para…
Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks
Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller
Curie's principle states that "when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them". We demonstrate that symmetry equivariant neu…