176 citations · 176 across the 1 of their papers we have counts for
3 papers
cs.LG2020
Natural Graph Networks
Pim de Haan, Taco Cohen, Max Welling
A key requirement for graph neural networks is that they must process a graph in a way that does not depend on how the graph is described. Traditionally this has been taken to mean…
cs.LG2019★ 176 cited
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…
cs.LG2018
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Maurice Weiler, Mario Geiger, Max Welling +2
We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equi…