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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Predictive Uncertainty Estimation via Prior Networks
Authors: Andrey Malinin, Mark Gales
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 16,872 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 15,306 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and MNIST and CIFAR-10 data show that unlike previous non-Bayesian methods PNs are able to distinguish between data and distributional uncertainty. |
| Researcher Affiliation | Academia | Andrey Malinin Department of Engineering University of Cambridge EMAIL Mark Gales Department of Engineering University of Cambridge EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly present in the paper. |
| Open Source Code | Yes | Code available at https://github.com/Kaos Engineer/Dirichlet Prior Networks |
| Open Datasets | Yes | An in-domain misclassification detection experiment and an out-of-distribution (OOD) input detection experiment were run on the MNIST and CIFAR-10 datasets [35, 36] to assess the DPN s ability to estimate uncertainty. |
| Dataset Splits | No | The misclassification detection experiment was run on the MNIST valid+test set and the CIFAR-10 test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) used to replicate the experiment. |
| Experiment Setup | No | The experimental setup is described in Appendix A and additional experiments are described in Appendix B. |