5 citations · 8 across the 2 of their papers we have counts for
2 papers
cs.LG2021★ 3 cited
Optimizing Sparse Matrix Multiplications for Graph Neural Networks
Shenghao Qiu, You Liang, Zheng Wang
Graph neural networks (GNNs) are emerging as a powerful technique for modeling graph structures. Due to the sparsity of real-world graph data, GNN performance is limited by extensi…
cs.LG2021★ 5 cited
Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy
Tim Hsu, Tuan Anh Pham, Nathan Keilbart +6
Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does n…