176 citations · 198 across the 3 of their papers we have counts for
9 papers
Coordinate Independent Convolutional Networks -- Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds
Maurice Weiler, Patrick Forré, Erik Verlinde +1
Motivated by the vast success of deep convolutional networks, there is a great interest in generalizing convolutions to non-Euclidean manifolds. A major complication in comparison…
A Wigner-Eckart Theorem for Group Equivariant Convolution Kernels
Leon Lang, Maurice Weiler
Group equivariant convolutional networks (GCNNs) endow classical convolutional networks with additional symmetry priors, which can lead to a considerably improved performance. Rece…
General -Equivariant Steerable CNNs
Maurice Weiler, Gabriele Cesa
The big empirical success of group equivariant networks has led in recent years to the sprouting of a great variety of equivariant network architectures. A particular focus has the…
Covariance in Physics and Convolutional Neural Networks
Miranda C. N. Cheng, Vassilis Anagiannis, Maurice Weiler +3
In this proceeding we give an overview of the idea of covariance (or equivariance) featured in the recent development of convolutional neural networks (CNNs). We study the similari…
Gauge Equivariant Convolutional Networks and the Icosahedral CNN
Taco S. Cohen, Maurice Weiler, Berkay Kicanaoglu +1
The principle of equivariance to symmetry transformations enables a theoretically grounded approach to neural network architecture design. Equivariant networks have shown excellent…
A General Theory of Equivariant CNNs on Homogeneous Spaces
Taco Cohen, Mario Geiger, Maurice Weiler
We present a general theory of Group equivariant Convolutional Neural Networks (G-CNNs) on homogeneous spaces such as Euclidean space and the sphere. Feature maps in these networks…