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I am very interested in your research _Ensemble Manifold Regularized Multi-Modal Graph Convolutional Network for Cognitive Ability Prediction_, and _Integrated Brain Connectivity Analysis with fMRI, D…
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# Ligand-based model
## Graph
Featurization:
- [x] Running
Model:
- [ ] Running
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Venue: ICML 2019
Summary: Proposes a simplified linear graph neural network architecture (GCN with non-linearity layers removed). New architecture is significantly faster than the state of the art mo…
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readme is a little bit simple.
I just started learning about graph neural networks,so i want to run through you code then learn your paper.
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ACL 24.07
ACL build command:
```
scons neon=1 opencl=0 openmp=1 cppthreads=0 os=linux data_layout_support=all arch=arm64-v8.2-a build=native --jobs=64 build=native --silent fixed_format_kernels=Tru…
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1.(HMMR) Learning 3d human dynamics from video(2019)
temporal encoder: **1D temporal** convolutional layers, **precompute** the image features on each frame, get current and ±∆t frames prediction.
c…
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Hi, I'm enjoying reading your code and paper. I have some questions about fixed graph and something in Graph Convolutional Model.
1. What 'hop_step' means in 'graph.py -> def get_adjacency(self, A…
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Hi, trying to learn spektral here. I can't seem to use ARMAConv:
def create_model(n_nodes, n_node_features, model_layers):
node_features_input = Input(shape=(n_node_features,), dtype=tf.float3…
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Fantastic work!
However, I’m a bit confused about the GNN encoding and 3D feature aspects of your project.
Although GNN encoding is referenced in your article, I couldn’t find the correspondin…
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## 一言でいうと
グラフにおいて「新しいノードをどう扱うか」という問題にチャレンジした研究。SNSやアイテム推薦における新規アイテム問題ではこの点が顕著だが、既存の研究では固定的なグラフを扱うのが大半だった。VAEをベースとして、順次ノードが追加されていく時系列の生成モデルという形でアプローチをしている
### 論文リンク
https://arxiv.org/abs/1903.…