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].
Asymptotic Properties for Bayesian Neural Network in Besov Space
Authors: Kyeongwon Lee, Jaeyong Lee
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present the obtained theoretical results in Section 3 and numerical examples in Section 4. A summary of the paper and a discussion are given in Section 5. We present the numerical experiments with the four functions in Appendix D. |
| Researcher Affiliation | Academia | Kyeongwon Lee Department of Statistics Seoul National University Seoul, Republic of Korea 08826 EMAIL Jaeyong Lee Department of Statistics Seoul National University Seoul, Republic of Korea 08826 EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | No | The paper defines mathematical functions (f1, f2, f3, f4) for numerical examples but does not provide concrete access information (link, DOI, repository) for a publicly available dataset used for training. Data is generated from these defined functions. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or explicit methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software components with their version numbers. |
| Experiment Setup | Yes | Tables 1 and 2 present the hyperparameters (model parameters) to estimate functions fi, i = 1, 2, 3, 4 with theoretical optimality. |