Mario Geiger

According to our database1, Mario Geiger authored at least 27 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
A recipe for cracking the quantum scaling limit with machine learned electron densities.
Mach. Learn. Sci. Technol., March, 2023

Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation.
CoRR, 2023

Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders.
CoRR, 2023

An end-to-end SE(3)-equivariant segmentation network.
CoRR, 2023

A General Framework for Equivariant Neural Networks on Reductive Lie Groups.
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.
Proceedings of the International Conference on Machine Learning, 2023

2022
e3nn: Euclidean Neural Networks.
CoRR, 2022

2021
How memory architecture affects performance and learning in simple POMDPs.
CoRR, 2021

Relative stability toward diffeomorphisms in deep nets indicates performance.
CoRR, 2021

SE(3)-equivariant prediction of molecular wavefunctions and electronic densities.
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.
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.
CoRR, 2020

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties.
CoRR, 2020

Compressing invariant manifolds in neural nets.
CoRR, 2020

Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks.
CoRR, 2020

2019
Disentangling feature and lazy learning in deep neural networks: an empirical study.
CoRR, 2019

Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm.
CoRR, 2019

Scaling description of generalization with number of parameters in deep learning.
CoRR, 2019

A General Theory of Equivariant CNNs on Homogeneous Spaces.
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.
CoRR, 2018

The jamming transition as a paradigm to understand the loss landscape of deep neural networks.
CoRR, 2018

Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks).
CoRR, 2018

3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data.
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.
Proceedings of the 35th International Conference on Machine Learning, 2018

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

2017
Convolutional Networks for Spherical Signals.
CoRR, 2017


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