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2020 – today
- 2024
[c9]Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt:
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation. ICLR 2024- [i24]Allan dos Santos Costa, Ilan Mitnikov, Franco Pellegrini, Ameya Daigavane, Mario Geiger, Zhonglin Cao, Karsten Kreis, Tess E. Smidt, Emine Küçükbenli, Joseph Jacobson:
EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants. CoRR abs/2410.09667 (2024) - 2023
- [j1]Joshua A. Rackers
, Lucas Tecot, Mario Geiger, Tess E. Smidt:
A recipe for cracking the quantum scaling limit with machine learned electron densities. Mach. Learn. Sci. Technol. 4(1): 15027 (2023) - [c8]Antonio Sclocchi, Mario Geiger, Matthieu Wyart:
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. ICML 2023: 30381-30405 - [c7]Ilyes Batatia, Mario Geiger, Jose M. Munoz, Tess E. Smidt, Lior Silberman, Christoph Ortner:
A General Framework for Equivariant Neural Networks on Reductive Lie Groups. NeurIPS 2023 - [i23]Antonio Sclocchi, Mario Geiger, Matthieu Wyart:
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. CoRR abs/2301.13703 (2023) - [i22]Ivan Diaz, Mario Geiger, Richard McKinley:
An end-to-end SE(3)-equivariant segmentation network. CoRR abs/2303.00351 (2023) - [i21]Ilyes Batatia, Mario Geiger, Jose M. Munoz, Tess E. Smidt, Lior Silberman, Christoph Ortner:
A General Framework for Equivariant Neural Networks on Reductive Lie Groups. CoRR abs/2306.00091 (2023) - [i20]Allan dos Santos Costa, Ilan Mitnikov, Mario Geiger, Manvitha Ponnapati, Tess E. Smidt, Joseph Jacobson:
Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders. CoRR abs/2310.02508 (2023) - [i19]Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt:
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation. CoRR abs/2311.16199 (2023) - 2022
- [i18]Mario Geiger, Tess E. Smidt:
e3nn: Euclidean Neural Networks. CoRR abs/2207.09453 (2022) - 2021
- [c6]Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart:
Relative stability toward diffeomorphisms indicates performance in deep nets. NeurIPS 2021: 8727-8739 - [c5]Oliver T. Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess E. Smidt, Klaus-Robert Müller:
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities. NeurIPS 2021: 14434-14447 - [i17]Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart:
Relative stability toward diffeomorphisms in deep nets indicates performance. CoRR abs/2105.02468 (2021) - [i16]Mario Geiger, Christophe Eloy, Matthieu Wyart:
How memory architecture affects performance and learning in simple POMDPs. CoRR abs/2106.08849 (2021) - 2020
- [i15]Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller:
Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks. CoRR abs/2007.02005 (2020) - [i14]Jonas Paccolat, Leonardo Petrini, Mario Geiger, Kevin Tyloo, Matthieu Wyart:
Compressing invariant manifolds in neural nets. CoRR abs/2007.11471 (2020) - [i13]Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé:
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties. CoRR abs/2008.08461 (2020) - [i12]Mario Geiger, Leonardo Petrini, Matthieu Wyart:
Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training. CoRR abs/2012.15110 (2020)
2010 – 2019
- 2019
- [c4]Taco S. Cohen, Mario Geiger, Maurice Weiler:
A General Theory of Equivariant CNNs on Homogeneous Spaces. NeurIPS 2019: 9142-9153 - [i11]Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d'Ascoli, Giulio Biroli, Clément Hongler, Matthieu Wyart:
Scaling description of generalization with number of parameters in deep learning. CoRR abs/1901.01608 (2019) - [i10]Stefano Spigler, Mario Geiger, Matthieu Wyart:
Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm. CoRR abs/1905.10843 (2019) - [i9]Mario Geiger, Stefano Spigler, Arthur Jacot, Matthieu Wyart:
Disentangling feature and lazy learning in deep neural networks: an empirical study. CoRR abs/1906.08034 (2019) - 2018
- [c3]Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling:
Spherical CNNs. ICLR 2018 - [c2]Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli:
Comparing Dynamics: Deep Neural Networks versus Glassy Systems. ICML 2018: 324-333 - [c1]Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen:
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. NeurIPS 2018: 10402-10413 - [i8]Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling:
Spherical CNNs. CoRR abs/1801.10130 (2018) - [i7]Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli:
Comparing Dynamics: Deep Neural Networks versus Glassy Systems. CoRR abs/1803.06969 (2018) - [i6]Taco S. Cohen, Mario Geiger, Maurice Weiler:
Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks). CoRR abs/1803.10743 (2018) - [i5]Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen:
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. CoRR abs/1807.02547 (2018) - [i4]Mario Geiger, Stefano Spigler, Stéphane d'Ascoli, Levent Sagun, Marco Baity-Jesi, Giulio Biroli, Matthieu Wyart:
The jamming transition as a paradigm to understand the loss landscape of deep neural networks. CoRR abs/1809.09349 (2018) - [i3]Stefano Spigler, Mario Geiger, Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Matthieu Wyart:
A jamming transition from under- to over-parametrization affects loss landscape and generalization. CoRR abs/1810.09665 (2018) - [i2]Taco Cohen, Mario Geiger, Maurice Weiler:
A General Theory of Equivariant CNNs on Homogeneous Spaces. CoRR abs/1811.02017 (2018) - 2017
- [i1]Taco Cohen, Mario Geiger, Jonas Köhler, Max Welling:
Convolutional Networks for Spherical Signals. CoRR abs/1709.04893 (2017)
Coauthor Index
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