Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation
Authors: Kun Chen, Peng Ye, Hao Chen, kang chen, Tao Han, Wanli Ouyang, Tao Chen, LEI BAI
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments |
| Researcher Affiliation | Collaboration | 1 Fudan University 2 Shanghai Artificial Intelligence Laboratory |
| Pseudocode | No | The paper describes its model architecture and components, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/Open Earth Lab/FNP. |
| Open Datasets | Yes | We demonstrate the effectiveness of our methodology on the ERA5 dataset [27], a global atmospheric reanalysis archive... The ERA5 dataset can be downloaded from the official website of Climate Data Store (CDS) at https://cds.climate.copernicus.eu. |
| Dataset Splits | Yes | We divide the ERA5 data from 1979-2015 as the training set, 2016-2017 as validation set, and 2018 as test set. |
| Hardware Specification | Yes | The training is run on 4 NVIDIA Tesla A100 GPUs with a global batch size of 16, and takes approximately 2.5 days. The inference only needs a few minutes to perform data assimilation for a whole year on single A100 GPU. |
| Software Dependencies | No | The paper mentions implementing the model based on the 'neural processes family project' and using the 'AdamW optimizer', but does not specify version numbers for general software libraries or programming languages. |
| Experiment Setup | Yes | The FNP model is implemented based on the open-source code of the neural processes family project [14], and trained for 20 epochs using the AdamW optimizer [39] with a learning rate of 1e-4... The dimension of data embedding for default setting is 128 and the number N of NFLs is 4, and a Gaussian likelihood is used with a negative log-likelihood (NLL) loss. |