![](https://dblp.uni-trier.de/img/logo.ua.320x120.png)
![](https://dblp.uni-trier.de/img/dropdown.dark.16x16.png)
![](https://dblp.uni-trier.de/img/peace.dark.16x16.png)
Остановите войну!
for scientists:
![search dblp search dblp](https://dblp.uni-trier.de/img/search.dark.16x16.png)
![search dblp](https://dblp.uni-trier.de/img/search.dark.16x16.png)
default search action
Mario Geiger
Person information
Refine list
![note](https://dblp.uni-trier.de/img/note-mark.dark.12x12.png)
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
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 - 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 Iain 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
![](https://dblp.uni-trier.de/img/cog.dark.24x24.png)
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-08-08 20:13 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint