| km1994/nlp_paper_study |
3,373 |
|
0 |
0 |
over 2 years ago |
0 |
|
1 |
|
C++ |
| 该仓库主要记录 NLP 算法工程师相关的顶会论文研读笔记 |
| danielegrattarola/spektral |
2,317 |
|
0 |
6 |
about 2 years ago |
34 |
June 01, 2023 |
67 |
mit |
Python |
| Graph Neural Networks with Keras and Tensorflow 2. |
| PetarV-/GAT |
2,078 |
|
0 |
0 |
over 4 years ago |
0 |
|
27 |
mit |
Python |
| Graph Attention Networks (https://arxiv.org/abs/1710.10903) |
| Diego999/pyGAT |
1,684 |
|
0 |
0 |
over 4 years ago |
0 |
|
32 |
mit |
Python |
| Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903) |
| xiangwang1223/knowledge_graph_attention_network |
434 |
|
0 |
0 |
over 5 years ago |
0 |
|
24 |
mit |
Python |
| KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019 |
| nakaizura/Source-Code-Notebook |
428 |
|
0 |
0 |
over 3 years ago |
0 |
|
3 |
|
Python |
| 关于一些经典论文源码的逐行中文笔记 |
| Jiakui/awesome-gcn |
377 |
|
0 |
0 |
almost 7 years ago |
0 |
|
1 |
|
|
| resources for graph convolutional networks （图卷积神经网络相关资源） |
| lrjconan/GRAN |
363 |
|
0 |
0 |
almost 4 years ago |
0 |
|
7 |
mit |
C++ |
| Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019 |
| inyeoplee77/SAGPool |
338 |
|
0 |
0 |
over 2 years ago |
0 |
|
10 |
|
Python |
| Official PyTorch Implementation of SAGPool - ICML 2019 |
| benedekrozemberczki/APPNP |
322 |
|
0 |
0 |
over 3 years ago |
0 |
|
0 |
gpl-3.0 |
Python |
| A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019). |
