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].
Can neural operators always be continuously discretized?
Authors: Takashi Furuya, Michael Puthawala, Matti Lassas, Maarten V. de Hoop
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper is theoretical. It does not have limitations in the same was as a more applied paper. All theorems and propositions are proved to be true, and so are not limited in their scope. |
| Researcher Affiliation | Academia | 1Shimane University, EMAIL 2South Dakota State University, EMAIL 3Rice University, EMAIL 4University of Helsinki, EMAIL |
| Pseudocode | No | The paper does not contain any sections explicitly labeled 'Pseudocode' or 'Algorithm', nor are there any structured code-like algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention the release of any source code. The NeurIPS checklist explicitly states, 'Our paper does not include experiments requiring code.' |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with datasets, thus no information about public dataset access is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments, therefore no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments, therefore no experimental setup details like hyperparameters or training settings are provided. |