Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 54,665 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,612 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |