Mario Geiger - RankMe

41 papers A* 10A 1Journal 29Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow. ICML Zhonglin Cao, Mario Geiger, Allan dos Santos Costa, Danny Reidenbach, Karsten Kreis, Tomas Geffner, Franco Pellegrini, Guoqing Zhou, Emine Küçükbenli
2025	J	`jnl`	Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow. CoRR Zhonglin Cao, Mario Geiger, Allan dos Santos Costa, Danny Reidenbach, Karsten Kreis, Tomas Geffner, Franco Pellegrini, Guoqing Zhou, Emine Küçükbenli
2025	A	`conf`	Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE. HPDC Jesun Sahariar Firoz, Franco Pellegrini, Mario Geiger, Darren Hsu, Jenna A. Bilbrey, Han-Yi Chou, Maximilian Stadler, Markus Höhnerbach, Tingyu Wang, Dejun Lin, Emine Küçükbenli, Henry W. Sprueill, Ilyes Batatia, Sotiris S. Xantheas, MalSoon Lee, Christopher J. Mundy, Gábor Csányi, Justin S. Smith, Ponnuswamy Sadayappan, Sutanay Choudhury
2025	J	`jnl`	Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE. CoRR 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, Ilyes Batatia, Sotiris S. Xantheas, MalSoon Lee, Chris Mundy, Gábor Csányi, Justin S. Smith, Ponnuswamy Sadayappan, Sutanay Choudhury
2025	A*	`conf`	Proteina: Scaling Flow-based Protein Structure Generative Models. ICLR Tomas Geffner, Kieran Didi, Zuobai Zhang, Danny Reidenbach, Zhonglin Cao, Jason Yim, Mario Geiger, Christian Dallago, Emine Küçükbenli, Arash Vahdat, Karsten Kreis
2025	J	`jnl`	Proteina: Scaling Flow-based Protein Structure Generative Models. CoRR Tomas Geffner, Kieran Didi, Zuobai Zhang, Danny Reidenbach, Zhonglin Cao, Jason Yim, Mario Geiger, Christian Dallago, Emine Küçükbenli, Arash Vahdat, Karsten Kreis
2024	J	`jnl`	BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery. CoRR 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, Akosua Busia, Abhishaike Mahajan, Stephen Malina, Neha Prasad, Sam Sinai, Lindsay Edwards, Thomas Gaudelet, Cristian Regep, Martin Steinegger, Burkhard Rost, Alexander Brace, Kyle Hippe, Luca Naef, Keisuke Kamata, George Armstrong, Kevin Boyd, Zhonglin Cao, Han-Yi Chou, Simon Chu, Allan dos Santos Costa, Sajad Darabi, Eric Dawson, Kieran Didi, Cong Fu, Mario Geiger, Michelle Gill, Darren Hsu, Gagan Kaushik, Maria Korshunova, Steven Kothen-Hill, Youhan Lee, Meng Liu, Micha Livne, Zachary McClure, Jonathan Mitchell, Alireza Moradzadeh, Ohad Mosafi, Youssef Nashed, Saee Paliwal, Yuxing Peng, Sara Rabhi, Farhad Ramezanghorbani, Danny Reidenbach, Camir Ricketts, Brian Roland, Kushal Shah, Tyler Shimko, Hassan Sirelkhatim, Savitha Srinivasan, Abraham C. Stern, Dorota Toczydlowska, Srimukh Prasad Veccham, Niccolò Alberto Elia Venanzi, Anton Vorontsov, Jared Wilber, Isabel Wilkinson, Wei Jing Wong, Eva Xue, Cory Ye, Xin Yu, Yang Zhang, Guoqing Zhou, Becca Zandstein, Christian Dallago, Bruno Trentini, Emine Küçükbenli, Timur Rvachov, Eddie Calleja, Johnny Israeli, Harry Clifford, Risto Haukioja, Nicholas Haemel, Kyle Tretina, Neha Tadimeti, Anthony B. Costa
2024	J	`jnl`	EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants. CoRR Allan dos Santos Costa, Ilan Mitnikov, Franco Pellegrini, Ameya Daigavane, Mario Geiger, Zhonglin Cao, Karsten Kreis, Tess E. Smidt, Emine Küçükbenli, Joseph Jacobson
2024	A*	`conf`	Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation. ICLR Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt
2023	A*	`conf`	A General Framework for Equivariant Neural Networks on Reductive Lie Groups. NeurIPS Ilyes Batatia, Mario Geiger, Jose M. Munoz, Tess E. Smidt, Lior Silberman, Christoph Ortner
2023	J	`jnl`	A General Framework for Equivariant Neural Networks on Reductive Lie Groups. CoRR Ilyes Batatia, Mario Geiger, Jose M. Munoz, Tess E. Smidt, Lior Silberman, Christoph Ortner
2023	J	`jnl`	A recipe for cracking the quantum scaling limit with machine learned electron densities. Mach. Learn. Sci. Technol. Joshua A. Rackers, Lucas Tecot, Mario Geiger, Tess E. Smidt
2023	J	`jnl`	An end-to-end SE(3)-equivariant segmentation network. CoRR Ivan Diaz, Mario Geiger, Richard McKinley
2023	A*	`conf`	Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. ICML Antonio Sclocchi, Mario Geiger, Matthieu Wyart
2023	J	`jnl`	Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. CoRR Antonio Sclocchi, Mario Geiger, Matthieu Wyart
2023	J	`jnl`	Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders. CoRR Allan dos Santos Costa, Ilan Mitnikov, Mario Geiger, Manvitha Ponnapati, Tess E. Smidt, Joseph Jacobson
2023	J	`jnl`	Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation. CoRR Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt
2022	J	`jnl`	e3nn: Euclidean Neural Networks. CoRR Mario Geiger, Tess E. Smidt
2021	J	`jnl`	How memory architecture affects performance and learning in simple POMDPs. CoRR Mario Geiger, Christophe Eloy, Matthieu Wyart
2021	J	`jnl`	Relative stability toward diffeomorphisms in deep nets indicates performance. CoRR Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart
2021	A*	`conf`	Relative stability toward diffeomorphisms indicates performance in deep nets. NeurIPS Leonardo Petrini, Alessandro Favero, Mario Geiger, Matthieu Wyart
2021	A*	`conf`	SE(3)-equivariant prediction of molecular wavefunctions and electronic densities. NeurIPS Oliver T. Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess E. Smidt, Klaus-Robert Müller
2020	J	`jnl`	Compressing invariant manifolds in neural nets. CoRR Jonas Paccolat, Leonardo Petrini, Mario Geiger, Kevin Tyloo, Matthieu Wyart
2020	J	`jnl`	Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks. CoRR Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller
2020	J	`jnl`	Perspective: A Phase Diagram for Deep Learning unifying Jamming, Feature Learning and Lazy Training. CoRR Mario Geiger, Leonardo Petrini, Matthieu Wyart
2020	J	`jnl`	Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties. CoRR Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé
2019	A*	`conf`	A General Theory of Equivariant CNNs on Homogeneous Spaces. NeurIPS Taco S. Cohen, Mario Geiger, Maurice Weiler
2019	J	`jnl`	Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm. CoRR Stefano Spigler, Mario Geiger, Matthieu Wyart
2019	J	`jnl`	Disentangling feature and lazy learning in deep neural networks: an empirical study. CoRR Mario Geiger, Stefano Spigler, Arthur Jacot, Matthieu Wyart
2019	J	`jnl`	Scaling description of generalization with number of parameters in deep learning. CoRR Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d'Ascoli, Giulio Biroli, Clément Hongler, Matthieu Wyart
2018	A*	`conf`	3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. NeurIPS Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen
2018	J	`jnl`	3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. CoRR Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen
2018	J	`jnl`	A General Theory of Equivariant CNNs on Homogeneous Spaces. CoRR Taco Cohen, Mario Geiger, Maurice Weiler
2018	J	`jnl`	A jamming transition from under- to over-parametrization affects loss landscape and generalization. CoRR Stefano Spigler, Mario Geiger, Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Matthieu Wyart
2018	A*	`conf`	Comparing Dynamics: Deep Neural Networks versus Glassy Systems. ICML Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli
2018	J	`jnl`	Comparing Dynamics: Deep Neural Networks versus Glassy Systems. CoRR Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gérard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli
2018	J	`jnl`	Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks). CoRR Taco S. Cohen, Mario Geiger, Maurice Weiler
2018	—	`conf`	Spherical CNNs. ICLR Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling
2018	J	`jnl`	Spherical CNNs. CoRR Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling
2018	J	`jnl`	The jamming transition as a paradigm to understand the loss landscape of deep neural networks. CoRR Mario Geiger, Stefano Spigler, Stéphane d'Ascoli, Levent Sagun, Marco Baity-Jesi, Giulio Biroli, Matthieu Wyart
2017	J	`jnl`	Convolutional Networks for Spherical Signals. CoRR Taco Cohen, Mario Geiger, Jonas Köhler, Max Welling