112 citations · 389 across the 35 of their papers we have counts for
35 papers
Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions
David R. Wessels, Farhad Ramezanghorbani, Alireza Moradzadeh +9
Subquadratic alternatives to attention require compromises when applied to multi-dimensional data: standard convolutions lack global receptive fields and input dependency, while re…
Orientation Scores should be a Piece of Cake
Finn M. Sherry, Chase van de Geijn, Erik J. Bekkers +1
We axiomatically derive a family of wavelets for an orientation score, lifting from position space to position and orientation space , with f…
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…
On genuine invariance learning without weight-tying
Artem Moskalev, Anna Sepliarskaia, Erik J. Bekkers +1
In this paper, we investigate properties and limitations of invariance learned by neural networks from the data compared to the genuine invariance achieved through invariant weight…
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…
Regular SE(3) Group Convolutions for Volumetric Medical Image Analysis
Thijs P. Kuipers, Erik J. Bekkers
Regular group convolutional neural networks (G-CNNs) have been shown to increase model performance and improve equivariance to different geometrical symmetries. This work addresses…