Mario Geiger
Cited by
Cited by
Spherical CNNs
TS Cohen, M Geiger, J Köhler, M Welling
ICML, 2018
3d steerable cnns: Learning rotationally equivariant features in volumetric data
M Weiler, M Geiger, M Welling, W Boomsma, T Cohen
arXiv preprint arXiv:1807.02547, 2018
A general theory of equivariant cnns on homogeneous spaces
T Cohen, M Geiger, M Weiler
arXiv preprint arXiv:1811.02017, 2018
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
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
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
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
Deep convolutional neural networks as strong gravitational lens detectors
C Schaefer, M Geiger, T Kuntzer, JP Kneib
Astronomy & Astrophysics 611, A2, 2018
The strong gravitational lens finding challenge
RB Metcalf, M Meneghetti, C Avestruz, F Bellagamba, CR Bom, E Bertin, ...
Astronomy & Astrophysics 625, A119, 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
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
Relevance of rotationally equivariant convolutions for predicting molecular properties
BK Miller, M Geiger, TE Smidt, F Noé
arXiv preprint arXiv:2008.08461, 2020
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
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
Se (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
S Batzner, TE Smidt, L Sun, JP Mailoa, M Kornbluth, N Molinari, ...
arXiv preprint arXiv:2101.03164, 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
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
Finding symmetry breaking order parameters with Euclidean neural networks
TE Smidt, M Geiger, BK Miller
Physical Review Research 3 (1), L012002, 2021
github. com/e3nn/e3nn
M Geiger, T Smidt, BK Miller, W Boomsma, K Lapchevskyi, M Weiler, ...
Version v0, 2020
Landscape and training regimes in deep learning
M Geiger, L Petrini, M Wyart
Physics Reports, 2021
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