Mario Geiger

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 33

Top Topics

Spherical Harmonics

Learning Curves

Top Venues

Publications

Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation. ICLR (2024)
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) (2023)
Antonio Sclocchi, Mario Geiger, Matthieu Wyart
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. ICML (2023)
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)
Ivan Diaz, Mario Geiger, Richard Iain McKinley
An end-to-end SE(3)-equivariant segmentation network. CoRR (2023)
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 (2023)
Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation. CoRR (2023)
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 (2023)
Antonio Sclocchi, Mario Geiger, Matthieu Wyart
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. CoRR (2023)
Mario Geiger, Tess Smidt
e3nn: Euclidean Neural Networks. CoRR (2022)
Mario Geiger, Christophe Eloy, Matthieu Wyart
How memory architecture affects performance and learning in simple POMDPs. CoRR (2021)
Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart
Relative stability toward diffeomorphisms in deep nets indicates performance. CoRR (2021)
Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart
Relative stability toward diffeomorphisms indicates performance in deep nets. NeurIPS (2021)
Oliver T. Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess Smidt, Klaus-Robert Müller
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities. NeurIPS (2021)
Jonas Paccolat, Leonardo Petrini, Mario Geiger, Kevin Tyloo, Matthieu Wyart
Compressing invariant manifolds in neural nets. CoRR (2020)
Mario Geiger, Leonardo Petrini, Matthieu Wyart
Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training. CoRR (2020)
Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties. CoRR (2020)
Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller
Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks. CoRR (2020)
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 (2019)
Stefano Spigler, Mario Geiger, Matthieu Wyart
Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm. CoRR (2019)
Taco S. Cohen, Mario Geiger, Maurice Weiler
A General Theory of Equivariant CNNs on Homogeneous Spaces. NeurIPS (2019)
Mario Geiger, Stefano Spigler, Arthur Jacot, Matthieu Wyart
Disentangling feature and lazy learning in deep neural networks: an empirical study. CoRR (2019)
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. CoRR (2018)
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. NeurIPS (2018)
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 (2018)
Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling
Spherical CNNs. CoRR (2018)
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 (2018)
Taco Cohen, Mario Geiger, Maurice Weiler
A General Theory of Equivariant CNNs on Homogeneous Spaces. CoRR (2018)
Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling
Spherical CNNs. ICLR (2018)
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 (2018)
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)
Taco S. Cohen, Mario Geiger, Maurice Weiler
Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks). CoRR (2018)
Taco Cohen, Mario Geiger, Jonas Köhler, Max Welling
Convolutional Networks for Spherical Signals. CoRR (2017)