31 citations · 125 across the 15 of their papers we have counts for
4 papers · 1 filter
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
David M. Knigge, David W. Romero, Erik J. Bekkers
Group convolutional neural networks (G-CNNs) have been shown to increase parameter efficiency and model accuracy by incorporating geometric inductive biases. In this work, we inves…
Learning Partial Equivariances from Data
David W. Romero, Suhas Lohit
Group Convolutional Neural Networks (G-CNNs) constrain learned features to respect the symmetries in the selected group, and lead to better generalization when these symmetries app…
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
David W. Romero, Robert-Jan Bruintjes, Jakub M. Tomczak +3
When designing Convolutional Neural Networks (CNNs), one must select the size\break of the convolutional kernels before training. Recent works show CNNs benefit from different kern…
CKConv: Continuous Kernel Convolution For Sequential Data
David W. Romero, Anna Kuzina, Erik J. Bekkers +2
Conventional neural architectures for sequential data present important limitations. Recurrent networks suffer from exploding and vanishing gradients, small effective memory horizo…