4 citations · 4 across the 1 of their papers we have counts for
14 papers
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…
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…
Disentangling feature and lazy training in deep neural networks
Mario Geiger, Stefano Spigler, Arthur Jacot +1
Two distinct limits for deep learning have been derived as the network width , depending on how the weights of the last layer scale with . In the Neural Tan…
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…
Scaling description of generalization with number of parameters in deep learning
Mario Geiger, Arthur Jacot, Stefano Spigler +6
Supervised deep learning involves the training of neural networks with a large number of parameters. For large enough , in the so-called over-parametrized regime, one can es…