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A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features

A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features

Jiuhai Chen
Jonas W. Mueller
V. Ioannidis
Tom Goldstein
David Wipf
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Papers citing "A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features"

4 / 4 papers shown
Title
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
Eli Chien
Wei-Cheng Chang
Cho-Jui Hsieh
Hsiang-Fu Yu
Jiong Zhang
O. Milenkovic
Inderjit S Dhillon
150
130
0
29 Oct 2021
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
Sergei Ivanov
Liudmila Prokhorenkova
AI4CE
51
52
0
21 Jan 2021
L$^2$-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
L2^2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
Yuning You
Tianlong Chen
Zhangyang Wang
Yang Shen
GNN
86
82
0
30 Mar 2020
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
Nick Erickson
Jonas W. Mueller
Alexander Shirkov
Hang Zhang
Pedro Larroy
Mu Li
Alex Smola
LMTD
84
605
0
13 Mar 2020