61 citations · 358 across the 40 of their papers we have counts for
5 papers · 1 filter
Approximately Equivariant Graph Networks
Ningyuan Huang, Ron Levie, Soledad Villar
Graph neural networks (GNNs) are commonly described as being permutation equivariant with respect to node relabeling in the graph. This symmetry of GNNs is often compared to the tr…
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning
Sharut Gupta, Joshua Robinson, Derek Lim +2
Self-supervised learning converts raw perceptual data such as images to a compact space where simple Euclidean distances measure meaningful variations in data. In this paper, we ex…
Fine-grained Expressivity of Graph Neural Networks
Jan Böker, Ron Levie, Ningyuan Huang +2
Numerous recent works have analyzed the expressive power of message-passing graph neural networks (MPNNs), primarily utilizing combinatorial techniques such as the -dimensional…
Equivariant geometric convolutions for emulation of dynamical systems
Wilson G. Gregory, David W. Hogg, Ben Blum-Smith +3
Machine learning methods are increasingly being employed as surrogate models in place of computationally expensive and slow numerical integrators for a bevy of applications in the…
Towards fully covariant machine learning
Soledad Villar, David W. Hogg, Weichi Yao +2
Any representation of data involves arbitrary investigator choices. Because those choices are external to the data-generating process, each choice leads to an exact symmetry, corre…