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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…