Spatio-Temporal Graph Convolutional Networks: Optimised Temporal Architecture
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915109662621696 |
|---|---|
| author | Turner, Edward |
| author_facet | Turner, Edward |
| contents | Spatio-Temporal graph convolutional networks were originally introduced with CNNs as temporal blocks for feature extraction. Since then LSTM temporal blocks have been proposed and shown to have promising results. We propose a novel architecture combining both CNN and LSTM temporal blocks and then provide an empirical comparison between our new and the pre-existing models. We provide theoretical arguments for the different temporal blocks and use a multitude of tests across different datasets to assess our hypotheses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10454 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spatio-Temporal Graph Convolutional Networks: Optimised Temporal Architecture Turner, Edward Machine Learning Spatio-Temporal graph convolutional networks were originally introduced with CNNs as temporal blocks for feature extraction. Since then LSTM temporal blocks have been proposed and shown to have promising results. We propose a novel architecture combining both CNN and LSTM temporal blocks and then provide an empirical comparison between our new and the pre-existing models. We provide theoretical arguments for the different temporal blocks and use a multitude of tests across different datasets to assess our hypotheses. |
| title | Spatio-Temporal Graph Convolutional Networks: Optimised Temporal Architecture |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.10454 |