Convolutional Networks for Spherical Signals. - Taco Cohen; Mario Geiger; Jonas Köhler 001; Max Welling (CoRR, 2017)

Spherical CNNs. - Taco S. Cohen; Mario Geiger; Jonas Köhler 001; Max Welling (ICLR, 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 (ICML, 2018)

3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. - Maurice Weiler; Mario Geiger; Max Welling; Wouter Boomsma; Taco Cohen (NeurIPS, 2018)

Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks). - Taco S. Cohen; Mario Geiger; Maurice Weiler (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)

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)

A General Theory of Equivariant CNNs on Homogeneous Spaces. - Taco S. Cohen; Mario Geiger; Maurice Weiler (NeurIPS, 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)

Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm. - Stefano Spigler; Mario Geiger; Matthieu Wyart (CoRR, 2019)

Disentangling feature and lazy learning in deep neural networks: an empirical study. - Mario Geiger; Stefano Spigler; Arthur Jacot; Matthieu Wyart (CoRR, 2019)

Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks. - Tess E. Smidt; Mario Geiger; Benjamin Kurt Miller (CoRR, 2020)

Compressing invariant manifolds in neural nets. - Jonas Paccolat; Leonardo Petrini; Mario Geiger; Kevin Tyloo; 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)

Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training. - Mario Geiger; Leonardo Petrini; Matthieu Wyart (CoRR, 2020)

Relative stability toward diffeomorphisms indicates performance in deep nets. - Leonardo Petrini; Alessandro Favero; Mario Geiger; Matthieu Wyart (NeurIPS, 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 (NeurIPS, 2021)

Relative stability toward diffeomorphisms in deep nets indicates performance. - Leonardo Petrini; Alessandro Favero; Mario Geiger; Matthieu Wyart (CoRR, 2021)

How memory architecture affects performance and learning in simple POMDPs. - Mario Geiger; Christophe Eloy; Matthieu Wyart (CoRR, 2021)

e3nn: Euclidean Neural Networks. - Mario Geiger; Tess E. Smidt (CoRR, 2022)

Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. - Antonio Sclocchi; Mario Geiger; Matthieu Wyart (ICML, 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 (NeurIPS, 2023)

An end-to-end SE(3)-equivariant segmentation network. - Ivan Diaz; Mario Geiger; Richard McKinley (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)

Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation. - Ameya Daigavane; Song Kim; Mario Geiger; Tess E. Smidt (CoRR, 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., 2023)

Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation. - Ameya Daigavane; Song Kim; Mario Geiger; Tess E. Smidt (ICLR, 2024)

EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants. - Allan dos Santos Costa; Ilan Mitnikov; Franco Pellegrini; Ameya Daigavane; Mario Geiger; Zhonglin Cao; Karsten Kreis; Tess E. Smidt; Emine Küçükbenli; Joseph Jacobson (CoRR, 2024)

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery. - Peter St. John; Dejun Lin; Polina Binder; Malcolm Greaves; Vega Shah; John St. John; Adrian Lange; Patrick D. Hsu; Rajesh Illango; Arvind Ramanathan; Anima Anandkumar; David H. Brookes; et al. (CoRR, 2024)

Proteina: Scaling Flow-based Protein Structure Generative Models. - Tomas Geffner; Kieran Didi; Zuobai Zhang; Danny Reidenbach; Zhonglin Cao; Jason Yim; Mario Geiger; Christian Dallago; Emine Küçükbenli; Arash Vahdat; Karsten Kreis (ICLR, 2025)

Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE. - Jesun Firoz; Franco Pellegrini; Mario Geiger; Darren Hsu; Jenna A. Bilbrey; Han-Yi Chou; Maximilian Stadler; Markus Hoehnerbach; Tingyu Wang; Dejun Lin; Emine Küçükbenli; Henry W. Sprueill; et al. (CoRR, 2025)

Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow. - Zhonglin Cao; Mario Geiger; Allan dos Santos Costa; Danny Reidenbach; Karsten Kreis; Tomas Geffner; Franco Pellegrini; Guoqing Zhou; Emine Küçükbenli (CoRR, 2025)