Mario Geiger
École Polytechnique Fédérale de Lausanne
H-index: 19
Europe-Switzerland
Top articles of Mario Geiger
Title | Journal | Author(s) | Publication Date |
|---|---|---|---|
Phonon predictions with E (3)-equivariant graph neural networks | arXiv preprint arXiv:2403.11347 | Shiang Fang Mario Geiger Joseph G Checkelsky Tess Smidt | 2024/3/17 |
A general framework for equivariant neural networks on reductive Lie groups | Advances in Neural Information Processing Systems | Ilyes Batatia Mario Geiger Jose Munoz Tess Smidt Lior Silberman | 2024/2/13 |
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation | arXiv preprint arXiv:2311.16199 | Ameya Daigavane Song Kim Mario Geiger Tess Smidt | 2023/11/27 |
Diffeomorphisms invariance is a proxy of performance in deep neural networks | APS March Meeting Abstracts | Leonardo Petrini Alessandro Favero Mario Geiger Matthieu Wyart | 2023 |
Modeling Future Plasma Performance in the HSX Stellarator with a new 70 GHz Gyrotron and Neutral Beam Injection | APS Division of Plasma Physics Meeting Abstracts | Benedikt Geiger David Anderson Joseph Talmadge Alexander Thornton HSX Team | 2023 |
Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO (3)-Equivariant Autoencoders | arXiv preprint arXiv:2310.02508 | Allan dos Santos Costa Ilan Mitnikov Mario Geiger Manvitha Ponnapati Tess Smidt | 2023/10/4 |
How SGD noise affects performance in distinct regimes of deep learning | APS March Meeting Abstracts | Antonio Sclocchi Mario Geiger Matthieu Wyart | 2023 |
Dissecting the effects of SGD noise in distinct regimes of deep learning | Antonio Sclocchi Mario Geiger Matthieu Wyart | 2023/7/3 | |
An end-to-end SE (3)-equivariant segmentation network | arXiv preprint arXiv:2303.00351 | Ivan Diaz Mario Geiger Richard Iain McKinley | 2023/3/1 |
A recipe for cracking the quantum scaling limit with machine learned electron densities | Machine Learning: Science and Technology | Joshua A Rackers Lucas Tecot Mario Geiger Tess E Smidt | 2023/2/27 |
e3nn: Euclidean neural networks | arXiv preprint arXiv:2207.09453 | Mario Geiger Tess Smidt | 2022/7/18 |
Cracking the quantum scaling limit with machine learned electron densities | arXiv preprint arXiv:2201.03726 | Joshua A Rackers Lucas Tecot Mario Geiger Tess E Smidt | 2022/1/11 |
SE (3)-equivariant prediction of molecular wavefunctions and electronic densities | Advances in Neural Information Processing Systems (NeurIPS) | Oliver T Unke Mihail Bogojeski Michael Gastegger Mario Geiger Tess Smidt | 2021/6/4 |
Landscape and training regimes in deep learning | Mario Geiger Leonardo Petrini Matthieu Wyart | 2021/8/15 | |
Geometric compression of invariant manifolds in neural networks | Journal of Statistical Mechanics: Theory and Experiment | Jonas Paccolat Leonardo Petrini Mario Geiger Kevin Tyloo Matthieu Wyart | 2021/4/26 |
SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials | arXiv preprint arXiv:2101.03164 | Simon Batzner Albert Musaelian Lixin Sun Mario Geiger Jonathan P. Mailoa | 2021/1/8 |
Finding symmetry breaking order parameters with Euclidean neural networks | Physical Review Research | Tess E Smidt Mario Geiger Benjamin Kurt Miller | 2021/1/4 |
Loss landscape and symmetries in Neural Networks | Mario Geiger | 2021 | |
Asymptotic learning curves of kernel methods: empirical data versus teacher–student paradigm | Journal of Statistical Mechanics: Theory and Experiment | Stefano Spigler Mario Geiger Matthieu Wyart | 2020/12/21 |
Disentangling feature and lazy training in deep neural networks | Journal of Statistical Mechanics: Theory and Experiment | Mario Geiger Stefano Spigler Arthur Jacot Matthieu Wyart | 2020/11/26 |
Co-Authors
