Mario Geiger

According to our database, Mario Geiger authored at least 27 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation.

Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

A recipe for cracking the quantum scaling limit with machine learned electron densities.

Joshua A. Rackers, Lucas Tecot, Mario Geiger, Tess E. Smidt

Mach. Learn. Sci. Technol., March, 2023

Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation.

Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt

CoRR, 2023

Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders.

Allan dos Santos Costa, Ilan Mitnikov, Mario Geiger, Manvitha Ponnapati, Tess E. Smidt, Joseph Jacobson

CoRR, 2023

An end-to-end SE(3)-equivariant segmentation network.

Ivan Diaz, Mario Geiger, Richard Iain McKinley

CoRR, 2023

A General Framework for Equivariant Neural Networks on Reductive Lie Groups.

Ilyes Batatia, Mario Geiger, Jose M. Munoz, Tess E. Smidt, Lior Silberman, Christoph Ortner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning.

Antonio Sclocchi, Mario Geiger, Matthieu Wyart

Proceedings of the International Conference on Machine Learning, 2023

2022

e3nn: Euclidean Neural Networks.

Mario Geiger, Tess E. Smidt

CoRR, 2022

2021

How memory architecture affects performance and learning in simple POMDPs.

Mario Geiger, Christophe Eloy, Matthieu Wyart

CoRR, 2021

Relative stability toward diffeomorphisms in deep nets indicates performance.

Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart

CoRR, 2021

SE(3)-equivariant prediction of molecular wavefunctions and electronic densities.

Oliver T. Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess E. Smidt, Klaus-Robert Müller

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Relative stability toward diffeomorphisms indicates performance in deep nets.

Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training.

Mario Geiger, Leonardo Petrini, Matthieu Wyart

CoRR, 2020

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties.

Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé

CoRR, 2020

Compressing invariant manifolds in neural nets.

Jonas Paccolat, Leonardo Petrini, Mario Geiger, Kevin Tyloo, Matthieu Wyart

CoRR, 2020

Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks.

Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller

CoRR, 2020

2019

Disentangling feature and lazy learning in deep neural networks: an empirical study.

Mario Geiger, Stefano Spigler, Arthur Jacot, Matthieu Wyart

CoRR, 2019

Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm.

Stefano Spigler, Mario Geiger, Matthieu Wyart

CoRR, 2019

Scaling description of generalization with number of parameters in deep learning.

Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d'Ascoli, Giulio Biroli, Clément Hongler, Matthieu Wyart

CoRR, 2019

A General Theory of Equivariant CNNs on Homogeneous Spaces.

Taco S. Cohen, Mario Geiger, Maurice Weiler

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

A jamming transition from under- to over-parametrization affects loss landscape and generalization.

Stefano Spigler, Mario Geiger, Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Matthieu Wyart

CoRR, 2018

The jamming transition as a paradigm to understand the loss landscape of deep neural networks.

Mario Geiger, Stefano Spigler, Stéphane d'Ascoli, Levent Sagun, Marco Baity-Jesi, Giulio Biroli, Matthieu Wyart

CoRR, 2018

Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks).

Taco S. Cohen, Mario Geiger, Maurice Weiler

CoRR, 2018

3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data.

Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Comparing Dynamics: Deep Neural Networks versus Glassy Systems.

Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli

Proceedings of the 35th International Conference on Machine Learning, 2018

Spherical CNNs.

Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Convolutional Networks for Spherical Signals.

Taco Cohen, Mario Geiger, Jonas Köhler, Max Welling

CoRR, 2017