26 citations · 114 across the 11 of their papers we have counts for
11 papers
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…
Equivariant Polynomials for Graph Neural Networks
Omri Puny, Derek Lim, Bobak T. Kiani +2
Graph Neural Networks (GNN) are inherently limited in their expressive power. Recent seminal works (Xu et al., 2019; Morris et al., 2019b) introduced the Weisfeiler-Lehman (WL) hie…
Sign and Basis Invariant Networks for Spectral Graph Representation Learning
Derek Lim, Joshua Robinson, Lingxiao Zhao +4
We introduce SignNet and BasisNet -- new neural architectures that are invariant to two key symmetries displayed by eigenvectors: (i) sign flips, since if is an eigenvector the…
Equivariant Subgraph Aggregation Networks
Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim +5
Message-passing neural networks (MPNNs) are the leading architecture for deep learning on graph-structured data, in large part due to their simplicity and scalability. Unfortunatel…
Equivariant Manifold Flows
Isay Katsman, Aaron Lou, Derek Lim +3
Tractably modelling distributions over manifolds has long been an important goal in the natural sciences. Recent work has focused on developing general machine learning models to l…
New Benchmarks for Learning on Non-Homophilous Graphs
Derek Lim, Xiuyu Li, Felix Hohne +1
Much data with graph structures satisfy the principle of homophily, meaning that connected nodes tend to be similar with respect to a specific attribute. As such, ubiquitous datase…