Spherical CNNs TS Cohen, M Geiger, J Köhler, M Welling International Conference on Machine Learning, 2018 | 1089 | 2018 |

SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials S Batzner, A Musaelian, L Sun, M Geiger, JP Mailoa, M Kornbluth, ... arXiv preprint arXiv:2101.03164, 2021 | 776 | 2021 |

3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data M Weiler, M Geiger, M Welling, W Boomsma, T Cohen Conference on Neural Information Processing Systems, 2018 | 499 | 2018 |

A General Theory of Equivariant CNNs on Homogeneous Spaces T Cohen, M Geiger, M Weiler Conference on Neural Information Processing Systems, 2019 | 354* | 2019 |

Scaling description of generalization with number of parameters in deep learning M Geiger, A Jacot, S Spigler, F Gabriel, L Sagun, S d’Ascoli, G Biroli, ... Journal of Statistical Mechanics: Theory and Experiment 2020 (2), 023401, 2020 | 206 | 2020 |

A jamming transition from under-to over-parametrization affects generalization in deep learning S Spigler, M Geiger, S d’Ascoli, L Sagun, G Biroli, M Wyart Journal of Physics A: Mathematical and Theoretical 52 (47), 474001, 2019 | 193* | 2019 |

Jamming transition as a paradigm to understand the loss landscape of deep neural networks M Geiger, S Spigler, S d'Ascoli, L Sagun, M Baity-Jesi, G Biroli, M Wyart Physical Review E 100 (1), 012115, 2019 | 160 | 2019 |

Disentangling feature and lazy training in deep neural networks M Geiger, S Spigler, A Jacot, M Wyart Journal of Statistical Mechanics: Theory and Experiment 2020 (11), 113301, 2020 | 120* | 2020 |

The strong gravitational lens finding challenge RB Metcalf, M Meneghetti, C Avestruz, F Bellagamba, CR Bom, E Bertin, ... Astronomy & Astrophysics 625, A119, 2019 | 119 | 2019 |

Comparing dynamics: Deep neural networks versus glassy systems M Baity-Jesi, L Sagun, M Geiger, S Spigler, GB Arous, C Cammarota, ... International Conference on Machine Learning, 314-323, 2018 | 118 | 2018 |

e3nn: Euclidean neural networks M Geiger, T Smidt arXiv preprint arXiv:2207.09453, 2022 | 102 | 2022 |

Deep convolutional neural networks as strong gravitational lens detectors C Schaefer, M Geiger, T Kuntzer, JP Kneib Astronomy & Astrophysics 611, A2, 2018 | 90 | 2018 |

Asymptotic learning curves of kernel methods: empirical data versus teacher–student paradigm S Spigler, M Geiger, M Wyart Journal of Statistical Mechanics: Theory and Experiment 2020 (12), 124001, 2020 | 73 | 2020 |

Relevance of rotationally equivariant convolutions for predicting molecular properties BK Miller, M Geiger, TE Smidt, F Noé arXiv preprint arXiv:2008.08461, 2020 | 71 | 2020 |

SE (3)-equivariant prediction of molecular wavefunctions and electronic densities O Unke, M Bogojeski, M Gastegger, M Geiger, T Smidt, KR Müller Advances in Neural Information Processing Systems 34, 14434-14447, 2021 | 68 | 2021 |

Finding symmetry breaking order parameters with Euclidean neural networks TE Smidt, M Geiger, BK Miller Physical Review Research 3 (1), L012002, 2021 | 41 | 2021 |

Landscape and training regimes in deep learning M Geiger, L Petrini, M Wyart Physics Reports 924, 1-18, 2021 | 40* | 2021 |

Geometric compression of invariant manifolds in neural networks J Paccolat, L Petrini, M Geiger, K Tyloo, M Wyart Journal of Statistical Mechanics: Theory and Experiment 2021 (4), 044001, 2021 | 37 | 2021 |

Thermal solar collector with VO2 absorber coating and V1-xWxO2 thermochromic glazing–Temperature matching and triggering A Paone, M Geiger, R Sanjines, A Schüler Solar energy 110, 151-159, 2014 | 31 | 2014 |

A recipe for cracking the quantum scaling limit with machine learned electron densities JA Rackers, L Tecot, M Geiger, TE Smidt Machine Learning: Science and Technology 4 (1), 015027, 2023 | 20 | 2023 |