[论文笔记]Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

时间 2020-07-25

标签论文笔记 cluster gcn efficient algorithm training deep large graph convolutional networks 繁體版

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文章目录 Abstract 1. Introduction Previous GCN Training Algorithms 2. Background 图卷积网络（GCNs） 3. Proposed Algorithm Why does vanilla mini-batch SGD have slow per-epoch time? Embedding utilization can refle

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1. 读 Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
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4. 论文笔记：An Efficient Hardware Accelerator for Structured Sparse Convolutional Neural Networks on FPGAs
5. 论文笔记：Quantizing deep convolutional networks for efficient inference: A whitepaper
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7. 《Simple and Deep Graph Convolutional Networks》--论文阅读笔记
8. 论文阅读笔记--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
9. 论文笔记《Very Deep Convolutional Networks for Large-Scale Image Recognition》
10. 论文笔记：LCML 2020 Simple and Deep Graph Convolutional Networks
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1. 读 Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
2. 论文笔记：Very Deep Convolutional Networks for Large-Scale Image Recognition
3. 论文笔记：An Efficient Hardware Accelerator for Sparse Convolutional Neural Networks on FPGAs
4. 论文笔记：An Efficient Hardware Accelerator for Structured Sparse Convolutional Neural Networks on FPGAs
5. 论文笔记：Quantizing deep convolutional networks for efficient inference: A whitepaper
6. 【论文阅读笔记】ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
7. 《Simple and Deep Graph Convolutional Networks》--论文阅读笔记
8. 论文阅读笔记--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
9. 论文笔记《Very Deep Convolutional Networks for Large-Scale Image Recognition》
10. 论文笔记：LCML 2020 Simple and Deep Graph Convolutional Networks

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