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Found 5 papers, 3 papers with code
TorchSparse: Efficient Point Cloud Inference Engine
TorchSparse directly optimizes the two bottlenecks of sparse convolution: irregular computation and data movement.
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
Graph neural networks (GNNs) have been increasingly deployed in various applications that involve learning on non-Euclidean data.
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other.
GARNET: A Spectral Approach to Robust and Scalable Graph Neural Networks
In this paper, we propose GARNET, a scalable spectral method to boost the adversarial robustness of GNN models for both homophilic and heterophilic graphs.
New Benchmarks for Learning on Non-Homophilous Graphs
Much data with graph structures satisfy the principle of homophily, meaning that connected nodes tend to be similar with respect to a specific attribute.
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