Mario Geiger
Title
Cited by
Cited by
Year
Spherical cnns
TS Cohen, M Geiger, J Köhler, M Welling
arXiv preprint arXiv:1801.10130, 2018
2582018
3d steerable cnns: Learning rotationally equivariant features in volumetric data
M Weiler, M Geiger, M Welling, W Boomsma, TS Cohen
Advances in Neural Information Processing Systems, 10381-10392, 2018
782018
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
472019
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
472018
A general theory of equivariant cnns on homogeneous spaces
TS Cohen, M Geiger, M Weiler
Advances in Neural Information Processing Systems, 9145-9156, 2019
442019
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
402020
Convolutional networks for spherical signals
T Cohen, M Geiger, J Köhler, M Welling
arXiv preprint arXiv:1709.04893, 2017
372017
A jamming transition from under-to over-parametrization affects loss landscape and generalization
S Spigler, M Geiger, S d'Ascoli, L Sagun, G Biroli, M Wyart
arXiv preprint arXiv:1810.09665, 2018
282018
Deep convolutional neural networks as strong gravitational lens detectors
C Schaefer, M Geiger, T Kuntzer, JP Kneib
Astronomy & Astrophysics 611, A2, 2018
272018
Intertwiners between induced representations (with applications to the theory of equivariant neural networks)
TS Cohen, M Geiger, M Weiler
arXiv preprint arXiv:1803.10743, 2018
212018
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
202014
The strong gravitational lens finding challenge
RB Metcalf, M Meneghetti, C Avestruz, F Bellagamba, CR Bom, E Bertin, ...
Astronomy & Astrophysics 625, A119, 2019
182019
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
162019
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
92012
Disentangling feature and lazy learning in deep neural networks: an empirical study
M Geiger, S Spigler, A Jacot, M Wyart
arXiv preprint arXiv:1906.08034, 2019
82019
Asymptotic learning curves of kernel methods: empirical data vs Teacher-Student paradigm
S Spigler, M Geiger, M Wyart
arXiv preprint arXiv:1905.10843, 2019
62019
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
62016
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. 2018
M Weiler, M Geiger, M Welling, W Boomsma, T Cohen
URL http://arxiv. org/abs, 1807
61807
Disentangling feature and lazy training in deep neural networks
M Geiger, S Spigler, A Jacot, M Wyart
arXiv preprint arXiv:1906.08034, 2019
12019
Compressing invariant manifolds in neural nets
J Paccolat, L Petrini, M Geiger, K Tyloo, M Wyart
arXiv preprint arXiv:2007.11471, 2020
2020
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