31 citations · 125 across the 15 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
Group Equivariant Stand-Alone Self-Attention For Vision
David W. Romero, Jean-Baptiste Cordonnier
We provide a general self-attention formulation to impose group equivariance to arbitrary symmetry groups. This is achieved by defining positional encodings that are invariant to t…
Attentive Group Equivariant Convolutional Networks
David W. Romero, Erik J. Bekkers, Jakub M. Tomczak +1
Although group convolutional networks are able to learn powerful representations based on symmetry patterns, they lack explicit means to learn meaningful relationships among them (…
Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring In Data
David W. Romero, Mark Hoogendoorn
Equivariance is a nice property to have as it produces much more parameter efficient neural architectures and preserves the structure of the input through the feature mapping. Even…