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图机器学习峰会-1-6 深度图卷积神经网络模型探索.pdf
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2022-07-05
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图机器学习峰会-1-6 深度图卷积神经网络模型探索.pdf
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Building Very Deep
Graph Neural Networks
for Representation
Learning on Graphs
Guohao Li
CS PhD Student @ KAUST
guohao.li@kaust.edu.sa
Building Very Deep Graph Neural Networks for Representation Learning on Graphs
Building Very Deep Graph Neural Networks for
Representation Learning on Graphs
4
Discussion:
To deep or not to deep
2
Making GCNs Go as
Deep as CNNs:
Message Aggregation
Functions;
Memory Efficiency
3
Designing GCNs
automatically:
Sequential Greedy
Architecture Search;
Latency Constrained;
1
Skip Connections and
Dilated Convolutions on
Graphs
Making GCNs Go as
Deep as CNNs:
Building Very Deep Graph Neural Networks for Representation Learning on Graphs
General Graphs:
● Social Networks
● Citation Networks
Lots of real-world applications need to deal with Non-Grid data
DeepGCNs.org
Graph data
Building Very Deep Graph Neural Networks for Representation Learning on Graphs
General Graphs:
● Social Networks
● Citation Networks
● Molecules
Lots of real-world applications need to deal with Non-Grid data
DeepGCNs.org
Graph data
Building Very Deep Graph Neural Networks for Representation Learning on Graphs
General Graphs:
● Social Networks
● Citation Networks
● Molecules
● Point Clouds
● 3D Meshes
● ...
Lots of real-world applications need to deal with Non-Grid data
DeepGCNs.org
Graph data
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