44 citations · 84 across the 7 of their papers we have counts for
6 papers · 1 filter
Lie Point Symmetry Data Augmentation for Neural PDE Solvers
Johannes Brandstetter, Max Welling, Daniel E. Worrall
Neural networks are increasingly being used to solve partial differential equations (PDEs), replacing slower numerical solvers. However, a critical issue is that neural PDE solvers…
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
Elise van der Pol, Daniel E. Worrall, Herke van Hoof +2
This paper introduces MDP homomorphic networks for deep reinforcement learning. MDP homomorphic networks are neural networks that are equivariant under symmetries in the joint stat…
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer +1
We introduce the SE(3)-Transformer, a variant of the self-attention module for 3D point clouds and graphs, which is equivariant under continuous 3D roto-translations. Equivariance…
Deep Scale-spaces: Equivariance Over Scale
Daniel E. Worrall, Max Welling
We introduce deep scale-spaces (DSS), a generalization of convolutional neural networks, exploiting the scale symmetry structure of conventional image recognition tasks. Put plainl…
Learning to Convolve: A Generalized Weight-Tying Approach
Nichita Diaconu, Daniel E Worrall
Recent work (Cohen & Welling, 2016) has shown that generalizations of convolutions, based on group theory, provide powerful inductive biases for learning. In these generalizations,…
Virtual Adversarial Ladder Networks For Semi-supervised Learning
Saki Shinoda, Daniel E. Worrall, Gabriel J. Brostow
Semi-supervised learning (SSL) partially circumvents the high cost of labeling data by augmenting a small labeled dataset with a large and relatively cheap unlabeled dataset drawn…