Search Results for author:
Found 2 papers, 0 papers with code
Weisfeiler Leman for Euclidean Equivariant Machine Learning
Finally, we show that a simple modification of this PPGN architecture can be used to obtain a universal equivariant architecture that can approximate all continuous equivariant functions uniformly.
Complete Neural Networks for Complete Euclidean Graphs
Neural networks for point clouds, which respect their natural invariance to permutation and rigid motion, have enjoyed recent success in modeling geometric phenomena, from molecular dynamics to recommender systems.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
