1.9k citations · 3.4k across the 26 of their papers we have counts for
26 papers
A General Framework for Equivariant Neural Networks on Reductive Lie Groups
Ilyes Batatia, Mario Geiger, Jose Munoz +3
Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, or the unitary groups, play essential roles across scientific fields as diverse as high energy physics, quan…
Leveraging SO(3)-steerable convolutions for pose-robust semantic segmentation in 3D medical data
Ivan Diaz, Mario Geiger, Richard Iain McKinley
Convolutional neural networks (CNNs) allow for parameter sharing and translational equivariance by using convolutional kernels in their linear layers. By restricting these kernels…
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning
Antonio Sclocchi, Mario Geiger, Matthieu Wyart
Understanding when the noise in stochastic gradient descent (SGD) affects generalization of deep neural networks remains a challenge, complicated by the fact that networks can oper…
e3nn: Euclidean Neural Networks
Mario Geiger, Tess Smidt
We present e3nn, a generalized framework for creating E(3) equivariant trainable functions, also known as Euclidean neural networks. e3nn naturally operates on geometry and geometr…
Cracking the Quantum Scaling Limit with Machine Learned Electron Densities
Joshua A. Rackers, Lucas Tecot, Mario Geiger +1
A long-standing goal of science is to accurately solve the Schrödinger equation for large molecular systems. The poor scaling of current quantum chemistry algorithms on classical c…
How memory architecture affects learning in a simple POMDP: the two-hypothesis testing problem
Mario Geiger, Christophe Eloy, Matthieu Wyart
Reinforcement learning is generally difficult for partially observable Markov decision processes (POMDPs), which occurs when the agent's observation is partial or noisy. To seek go…