5 citations · 14 across the 5 of their papers we have counts for
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cs.LG2023
Cached Operator Reordering: A Unified View for Fast GNN Training
Julia Bazinska, Andrei Ivanov, Tal Ben-Nun +4
Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering. However,…
cs.LG2021★ 4 cited
High-order Differentiable Autoencoder for Nonlinear Model Reduction
Siyuan Shen, Yang Yin, Tianjia Shao +4
This paper provides a new avenue for exploiting deep neural networks to improve physics-based simulation. Specifically, we integrate the classic Lagrangian mechanics with a deep au…
cs.LG2020★ 2 cited
Second-order Neural Network Training Using Complex-step Directional Derivative
Siyuan Shen, Tianjia Shao, Kun Zhou +3
While the superior performance of second-order optimization methods such as Newton's method is well known, they are hardly used in practice for deep learning because neither assemb…