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20172023
most citedE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

1.9k citations · 3.4k across the 26 of their papers we have counts for

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cond-mat.dis-nn2019★ 166 cited

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…

cond-mat.dis-nn2018★ 122 cited

The jamming transition as a paradigm to understand the loss landscape of deep neural networks

Mario Geiger, Stefano Spigler, Stéphane d'Ascoli +4

Deep learning has been immensely successful at a variety of tasks, ranging from classification to AI. Learning corresponds to fitting training data, which is implemented by descend…