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Mario Geiger
Mario Geiger
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Title
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Cited by
Year
Spherical CNNs
TS Cohen, M Geiger, J Köhler, M Welling
International Conference on Machine Learning, 2018
10512018
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
6822021
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
4802018
A General Theory of Equivariant CNNs on Homogeneous Spaces
T Cohen, M Geiger, M Weiler
Conference on Neural Information Processing Systems, 2019
346*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
2022020
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
187*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
1582019
The strong gravitational lens finding challenge
RB Metcalf, M Meneghetti, C Avestruz, F Bellagamba, CR Bom, E Bertin, ...
Astronomy & Astrophysics 625, A119, 2019
1202019
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
1182018
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
114*2020
e3nn: Euclidean neural networks
M Geiger, T Smidt
arXiv preprint arXiv:2207.09453, 2022
852022
Deep convolutional neural networks as strong gravitational lens detectors
C Schaefer, M Geiger, T Kuntzer, JP Kneib
Astronomy & Astrophysics 611, A2, 2018
852018
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
662020
Relevance of rotationally equivariant convolutions for predicting molecular properties
BK Miller, M Geiger, TE Smidt, F Noé
arXiv preprint arXiv:2008.08461, 2020
662020
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
632021
Landscape and training regimes in deep learning
M Geiger, L Petrini, M Wyart
Physics Reports 924, 1-18, 2021
41*2021
Finding symmetry breaking order parameters with Euclidean neural networks
TE Smidt, M Geiger, BK Miller
Physical Review Research 3 (1), L012002, 2021
392021
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
362021
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
302014
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
182023
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