《Diffusion-Convolutional Neural Networks》论文理解
1.DCNN框架 DCNN以节点的特征矩阵以及节点的概率转移矩阵（可以认为是结构矩阵）为输入，然后以每个节点为中心，将不同的跳（hop）上的节点信息进行聚合，得到前 H H H跳的聚合向量，构成节点的扩散表示 Z t ( i , : , : ) ∈ R H × F Z_t(i,:,:)\in R^{H\times F} Zt(i,:,:)∈RH×F，所有节点的扩散表示组成张量 Z t ∈ R N
相关文章
- 1. 【Binarized Neural Networks】论文笔记
- 2. [论文解读] Concolic Testing for Deep Neural Networks
- 3. 论文理解《Dense crowd counting from still images with convolutional neural networks》
- 4. 《Bag of Freebies for Training Object Detection Neural Networks》论文理解
- 5. Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks论文理解
- 6. 《GRAPH ATTENTION NETWORKS》论文理解
- 7. Inverting Convolutional Networks with Convolutional Networks 论文理解
- 8. Deep Neural Networks for Object Detection论文翻译
- 9. 论文ImageNet Classification with Deep Convolutional Neural Networks（Alexnet2012）
- 10. 论文《Convolutional Neural Networks for Sentence Classification》总结
- 更多相关文章...
- • CAP理论是什么？ - NoSQL教程
- • PHP 文件处理 - PHP教程
- • Scala 中文乱码解决
- • Docker 清理命令
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. 【Binarized Neural Networks】论文笔记
- 2. [论文解读] Concolic Testing for Deep Neural Networks
- 3. 论文理解《Dense crowd counting from still images with convolutional neural networks》
- 4. 《Bag of Freebies for Training Object Detection Neural Networks》论文理解
- 5. Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks论文理解
- 6. 《GRAPH ATTENTION NETWORKS》论文理解
- 7. Inverting Convolutional Networks with Convolutional Networks 论文理解
- 8. Deep Neural Networks for Object Detection论文翻译
- 9. 论文ImageNet Classification with Deep Convolutional Neural Networks（Alexnet2012）
- 10. 论文《Convolutional Neural Networks for Sentence Classification》总结