3 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.LG2021
Concentric Spherical GNN for 3D Representation Learning
James Fox, Bo Zhao, Sivasankaran Rajamanickam +2
Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and c…
cs.LG2019★ 3 cited
How Robust Are Graph Neural Networks to Structural Noise?
James Fox, Sivasankaran Rajamanickam
Graph neural networks (GNNs) are an emerging model for learning graph embeddings and making predictions on graph structured data. However, robustness of graph neural networks is no…
cs.DC2019
Performance Impact of Memory Channels on Sparse and Irregular Algorithms
Oded Green, James Fox, Jeffrey Young +2
Graph processing is typically considered to be a memory-bound rather than compute-bound problem. One common line of thought is that more available memory bandwidth corresponds to b…