
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs
A common approach to define convolutions on meshes is to interpret them ...
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General E(2)Equivariant Steerable CNNs
The big empirical success of group equivariant networks has led in recen...
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Covariance in Physics and Convolutional Neural Networks
In this proceeding we give an overview of the idea of covariance (or equ...
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Gauge Equivariant Convolutional Networks and the Icosahedral CNN
The idea of equivariance to symmetry transformations provides one of the...
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A General Theory of Equivariant CNNs on Homogeneous Spaces
Group equivariant convolutional neural networks (GCNNs) have recently e...
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Explorations in Homeomorphic Variational AutoEncoding
The manifold hypothesis states that many kinds of highdimensional data ...
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3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
We present a convolutional network that is equivariant to rigid body mot...
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Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks)
Group equivariant and steerable convolutional neural networks (regular a...
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Learning Steerable Filters for Rotation Equivariant CNNs
In many machine learning tasks it is desirable that a model's prediction...
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Maurice Weiler
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