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06/24/2023
Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning
Self-supervised learning converts raw perceptual data such as images to ...
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05/27/2023
Graph Inductive Biases in Transformers without Message Passing
Transformers for graph data are increasingly widely studied and successf...
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02/22/2023
Equivariant Polynomials for Graph Neural Networks
Graph Neural Networks (GNN) are inherently limited in their expressive p...
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10/27/2021
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
Many widely used datasets for graph machine learning tasks have generall...
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10/06/2021
Equivariant Subgraph Aggregation Networks
Message-passing neural networks (MPNNs) are the leading architecture for...
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07/19/2021
Equivariant Manifold Flows
Tractably modelling distributions over manifolds has long been an import...
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04/03/2021
New Benchmarks for Learning on Non-Homophilous Graphs
Much data with graph structures satisfy the principle of homophily, mean...
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11/30/2020
Doubly Stochastic Subspace Clustering
Many state-of-the-art subspace clustering methods follow a two-step proc...
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06/18/2020
Neural Manifold Ordinary Differential Equations
To better conform to data geometry, recent deep generative modelling tec...
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06/15/2020