Geiger, Mario

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., & Kozinsky, B. (2022) · Nature Communications

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

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The strong gravitational lens finding challenge

Metcalf, R. B., Meneghetti, M., Avestruz, C., Bellagamba, F., Bom, C. R., Bertin, E., Cabanac, R., Courbin, F., Davies, A., Decencière, E., et al. (2019) · Astronomy & Astrophysics

Galaxies: Formation, Evolution, PhenomenaAstrophysical Phenomena and Observations

Cited by 152

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

Geiger, M., Spigler, S., d'Ascoli, S., Sagun, L., Baity-Jesi, M., Biroli, G., & Wyart, M. (2019) · Physical Review E

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

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