| Spherical CNNs TS Cohen, M Geiger, J Köhler, M Welling International Conference on Machine Learning, 2018 | 568* | 2018 |
| 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 | 204 | 2018 |
| A General Theory of Equivariant CNNs on Homogeneous Spaces T Cohen, M Geiger, M Weiler Conference on Neural Information Processing Systems, 2019 | 164* | 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 | 114 | 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 | 114* | 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 | 96 | 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 | 84* | 2018 |
| The strong gravitational lens finding challenge RB Metcalf, M Meneghetti, C Avestruz, F Bellagamba, CR Bom, E Bertin, ... Astronomy & Astrophysics 625, A119, 2019 | 53 | 2019 |
| Deep convolutional neural networks as strong gravitational lens detectors C Schaefer, M Geiger, T Kuntzer, JP Kneib Astronomy & Astrophysics 611, A2, 2018 | 46 | 2018 |
| 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 | 40* | 2020 |
| 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 | 27 | 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 | 25 | 2014 |
| 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 | 23 | 2020 |
| Relevance of rotationally equivariant convolutions for predicting molecular properties BK Miller, M Geiger, TE Smidt, F Noé arXiv preprint arXiv:2008.08461, 2020 | 23 | 2020 |
| 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 | 13* | 2021 |
| Embedded microstructures for daylighting and seasonal thermal control A Kostro, M Geiger, N Jolissaint, MAG Lazo, JL Scartezzini, Y Leterrier, ... Nonimaging Optics: Efficient Design for Illumination and Solar Concentration …, 2012 | 12 | 2012 |
| CFSpro: ray tracing for design and optimization of complex fenestration systems using mixed dimensionality approach A Kostro, M Geiger, JL Scartezzini, A Schüler Applied optics 55 (19), 5127-5134, 2016 | 11 | 2016 |
| Finding symmetry breaking order parameters with Euclidean neural networks TE Smidt, M Geiger, BK Miller Physical Review Research 3 (1), L012002, 2021 | 9 | 2021 |
| Landscape and training regimes in deep learning M Geiger, L Petrini, M Wyart Physics Reports, 2021 | 7* | 2021 |
| SE (3)-equivariant prediction of molecular wavefunctions and electronic densities OT Unke, M Bogojeski, M Gastegger, M Geiger, T Smidt, KR Müller arXiv preprint arXiv:2106.02347, 2021 | 4 | 2021 |
Matthieu WyartEPFL, Institute of PhysicsVerified email at epfl.ch
