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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |