Chao Huang, Lianghao Xia, Zhonghang Li, Yong Xu
We lifted 11 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| HKUDS/GPT-ST | canonical | 1 of 1 |
| hkuds/gpt-st | — | 5 of 9 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| MLP_RL | Ran | hkuds/gpt-st/model/Pretrain_model/GPTST.py code served (permissive licence) · get_code("927d27d215fac2e4") |
| cap | Ran | hkuds/gpt-st/model/Pretrain_model/GPTST.py code served (permissive licence) · get_code("c3f305c9f9b9a9da") |
| hyperTem | Ran | hkuds/gpt-st/model/Pretrain_model/GPTST.py code served (permissive licence) · get_code("ddf013045810b713") |
| squash | Ran | HKUDS/GPT-ST/model/Pretrain_model/GPTST.py code served (permissive licence) · get_code("c0581450b12d51cb") |
| squash | Ran | this paper's copy was not recorded; identical code first harvested from Riroaki/CapsNet pointer only · get_code("89d981e0b618190e") |
| time_feature | Ran | hkuds/gpt-st/model/Pretrain_model/GPTST.py code served (permissive licence) · get_code("aefc4fd3ddd1ba80") |
| time_feature_spg | Ran | hkuds/gpt-st/model/Pretrain_model/GPTST.py code served (permissive licence) · get_code("63348d35e82a5555") |
| GPTST_Model | Not yet run | hkuds/gpt-st/model/Pretrain_model/GPTST.py code served (permissive licence) · get_code("9788e91af7881d61") |
| Hypergraph_decoder | Not yet run | hkuds/gpt-st/model/Pretrain_model/GPTST.py code served (permissive licence) · get_code("f77ed504389f9434") |
| Hypergraph_encoder | Not yet run | hkuds/gpt-st/model/Pretrain_model/GPTST.py code served (permissive licence) · get_code("df2eab2dbfd8e6e2") |
| STHCN | Not yet run | hkuds/gpt-st/model/Pretrain_model/GPTST.py code served (permissive licence) · get_code("1d039f8b5b7ac463") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In recent years, there has been a rapid development of spatio-temporal prediction techniques in response to the increasing demands of traffic management and travel planning. While advanced end-to-end models have achieved notable success in improving predictive performance, their integration and expansion pose significant challenges. This work aims to address these challenges by introducing a spatio-temporal pre-training framework that seamlessly integrates with downstream baselines and enhances their performance. The framework is built upon two key designs: (i) We propose a spatio-temporal mask autoencoder as a pre-training model for learning spatio-temporal dependencies. The model incorporates customized parameter learners and hierarchical spatial pattern encoding networks. These modules are specifically designed to capture spatio-temporal customized representations and intra-and inter-cluster region semantic relationships, which have often been neglected in existing approaches. (ii) We introduce an adaptive mask strategy as part of the pre-training mechanism. This strategy guides the mask autoencoder in learning robust spatio-temporal representations and facilitates the modeling of different relationships, ranging from intra-cluster to inter-cluster, in an easy-to-hard training manner. Extensive experiments conducted on representative benchmarks demonstrate the effectiveness of our proposed method. We have made our model implementation publicly available at https://github.com/HKUDS/GPT-ST.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2311.04245")
get_code_for_paper("2311.04245")
have("2311.04245")
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