16 citations · 51 across the 17 of their papers we have counts for
5 papers · 2 filters
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…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
An Exploration of Conditioning Methods in Graph Neural Networks
Yeskendir Koishekenov, Erik J. Bekkers
The flexibility and effectiveness of message passing based graph neural networks (GNNs) induced considerable advances in deep learning on graph-structured data. In such approaches,…
E(n) Equivariant Message Passing Simplicial Networks
Floor Eijkelboom, Rob Hesselink, Erik Bekkers
This paper presents Equivariant Message Passing Simplicial Networks (EMPSNs), a novel approach to learning on geometric graphs and point clouds that is equivariant…