31 citations · 125 across the 15 of their papers we have counts for
4 papers · 1 filter
Fast, Expressive SE Equivariant Networks through Weight-Sharing in Position-Orientation Space
Erik J Bekkers, Sharvaree Vadgama, Rob D Hesselink +2
Based on the theory of homogeneous spaces we derive geometrically optimal edge attributes to be used within the flexible message-passing framework. We formalize the notion of weigh…
Learned Gridification for Efficient Point Cloud Processing
Putri A. van der Linden, David W. Romero, Erik J. Bekkers
Neural operations that rely on neighborhood information are much more expensive when deployed on point clouds than on grid data due to the irregular distances between points in a p…
DNArch: Learning Convolutional Neural Architectures by Backpropagation
David W. Romero, Neil Zeghidour
We present Differentiable Neural Architectures (DNArch), a method that jointly learns the weights and the architecture of Convolutional Neural Networks (CNNs) by backpropagation. I…
Modelling Long Range Dependencies in D: From Task-Specific to a General Purpose CNN
David M. Knigge, David W. Romero, Albert Gu +5
Performant Convolutional Neural Network (CNN) architectures must be tailored to specific tasks in order to consider the length, resolution, and dimensionality of the input data. In…