【泛读】NIPS- Break the Ceiling:Stronger Multi-scale Deep GCNs
Break the Ceiling:Stronger Multi-scale Deep GCNs Whats the problem? 现存的图神经网络没有充分利用多尺度信息和深层架构。 1.如果基于GCN架构增加层数，提取的特征最多保留图结构的固定信息，并且丢失节点特征中的所有信息。 2.ReLU是一个阴谋家。 代码链接 How to solve them? 作者证明了任何具有明确定义的解析光谱
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相关文章
- 1. DeepGCNs- Can GCNs Go as Deep as CNNs?
- 2. Visual saliency detection based on multiscale deep cnn features
- 3. DeepGCNs-Can GCNs Go as Deep as CNNs? ICCV 2019
- 4. nips 208 visual question answering 导读
- 5. NIPS-2013：Playing Atari with Deep Reinforcement Learning
- 6. Paper-1 精读GAIL 2016 NIPS
- 7. 2017-04-18 Visual Saliency Detection Based on Multiscale Deep CNN Features
- 8. [NIPS 2018] Generalized Zero-Shot Learning with Deep Calibration Network
- 9. Paper-2 精读GPS (2014 NIPS)
- 10. ICCV2019点云论文 ResGCN-Can GCNs Go as Deep as CNNs