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We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of $\mathrm{E}(p,q)$-equivariant CNNs. CS-CNNs …

Equivariance is becoming an increasingly popular design choice to build data efficient neural networks by exploiting prior knowledge …

Recent work in equivariant deep learning bears strong similarities to physics. Fields over a base space are fundamental entities in …

Group equivariant convolutional networks (GCNNs) endow classical convolutional networks with additional symmetry priors, which can lead …

A common approach to define convolutions on meshes is to interpret them as a graph and apply graph convolutional networks (GCNs). Such …

The big empirical success of group equivariant networks has led in recent years to the sprouting of a great variety of equivariant …

The principle of equivariance to symmetry transformations enables a theoretically grounded approach to neural network architecture …

We present a general theory of Group equivariant Convolutional Neural Networks (G-CNNs) on homogeneous spaces such as Euclidean space …

We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over …

In many machine learning tasks it is desirable that a model’s prediction transforms in an equivariant way under transformations …