Hiroshi Kera, Ayumu Ueyama, Kazuhiko Kawamoto
We lifted 4 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| microsoft/ClimaX | canonical | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| collate_fn | Ran | microsoft/ClimaX/src/climax/climate_projection/datamodule.py code served (permissive licence) · get_code("ca6b23257d5d36ba") |
| collate_fn | Ran | microsoft/ClimaX/src/climax/pretrain/datamodule.py code served (permissive licence) · get_code("79e5404417b02493") |
| input_for_training | Ran | microsoft/ClimaX/src/climax/climate_projection/dataset.py code served (permissive licence) · get_code("53dc4b5a0983ed3b") |
| output_for_training | Ran | microsoft/ClimaX/src/climax/climate_projection/dataset.py code served (permissive licence) · get_code("cd313bef2343b1be") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Weather forecasting is essential for various human activities. Recent data-driven models have outperformed numerical weather prediction by utilizing deep learning in forecasting performance. However, challenges remain in efficiently handling multiple meteorological variables. This study proposes a new variable aggregation scheme and an efficient learning framework for that challenge. Experiments show that VarteX outperforms the conventional model in forecast performance, requiring significantly fewer parameters and resources. The effectiveness of learning through multiple aggregations and regional split training is demonstrated, enabling more efficient and accurate deep learning-based weather forecasting.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.19615")
get_code_for_paper("2406.19615")
have("2406.19615")
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