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'Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis' is a good paper with 3D-GCN, whose funciton contain graph classification and node cals…
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https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#fusion-types says:
**Depthwise Separable Convolution**
`A depthwise convolution with activation followed by a convolution w…
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Hi,
I have implemented the forward pass using a convolution + sigmoid_fwd activation and am now working on the backpropagation of the graph. However, according to the [document](https://docs.nvidia.…
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'Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis' is a good paper with 3D-GCN, whose funciton contain graph classification and node ca…
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When I quantize yolo v3 model flow this tutorial, I found that the output nodes in this code block should be `conv2d_59/BiasAdd,conv2d_67/BiasAdd,conv2d_75/BiasAdd` while not `conv2d_59/convolution…
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Hello @mdeff
Let me thank you for this notebook showing different graph convolution implementation.
https://github.com/mdeff/cnn_graph/blob/master/trials/1_learning_filters.ipynb
l'm wonderi…
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Permuting the atom order leads to different output probabilities. In this case I've permuted the ordering of the atoms in strychnine and a miyazaki graph about 1000 times using the following code, the…
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#### Heterogeneous GNN
- [ ] Relational GCN (+ DistMult)
- [x] mini-batch learning (DGL)
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Current model only takes into account 1D interactions.
It should be possible to define a graph (for instance, via [Delaunay triangulation](https://en.wikipedia.org/wiki/Delaunay_triangulation)) and …
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Title: - Semi-supervised User Geolocation via Graph Convolutional Networks
Year: - 2018
Venue: - arXiv
**Main Problem**
The authors addresses the problem of inaccurate prediction of user geo-loc…