| microsoft/DeBERTa |
1,673 |
|
0 |
0 |
over 2 years ago |
13 |
February 09, 2021 |
63 |
mit |
Python |
| The implementation of DeBERTa |
| alohays/awesome-visual-representation-learning-with-transformers |
209 |
|
0 |
0 |
over 4 years ago |
0 |
|
0 |
mit |
|
| Awesome Transformers (self-attention) in Computer Vision |
| iamhankai/attribute-aware-attention |
124 |
|
0 |
0 |
over 6 years ago |
0 |
|
2 |
|
Python |
| [ACM MM 2018] Attribute-Aware Attention Model for Fine-grained Representation Learning |
| vijaydwivedi75/gnn-lspe |
114 |
|
0 |
0 |
about 4 years ago |
0 |
|
0 |
mit |
Python |
| Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022 |
| dawnranger/pytorch-AGNN |
94 |
|
0 |
0 |
over 8 years ago |
0 |
|
1 |
mit |
Python |
| Pytorch implementation of the Attention-based Graph Neural Network(AGNN) |
| all-things-vits/code-samples |
42 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
apache-2.0 |
Jupyter Notebook |
| Holds code for our CVPR'23 tutorial: All Things ViTs: Understanding and Interpreting Attention in Vision. |
| brochier/idne |
13 |
|
0 |
0 |
almost 6 years ago |
0 |
|
1 |
|
Python |
| Python package for the paper "Inductive Document Network Embedding with Topic-Word Attention" (https://arxiv.org/pdf/2001.03369.pdf) |
| sumehta/FBMA |
5 |
|
0 |
0 |
over 5 years ago |
0 |
|
0 |
mit |
Python |
| Code for the WWW '19 paper "Event Detection using Hierarchical Multi-Aspect Attention" |
