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Shunhua Jiang

2 accepted papers

2022

Fast Graph Neural Tangent Kernel via Kronecker Sketching

AAAI 2022technical

Many deep learning tasks need to deal with graph data (e.g., social networks, protein structures, code ASTs). Due to the importance of these tasks, people turned to Graph Neural Networks (GNNs) as the de facto method for machine learning on graph data. GNNs have become widely applied due to their co…

Cited by 8SourcePDFScholar
2017

Learning Gradient Descent: Better Generalization and Longer Horizons

ICML 2017poster

Training deep neural networks is a highly nontrivial task, involving carefully selecting appropriate training algorithms, scheduling step sizes and tuning other hyperparameters. Trying different combinations can be quite labor-intensive and time consuming. Recently, researchers have tried to use dee…