Predictive Uncertainty Estimation via Prior Networks
Authors: Andrey Malinin, Mark Gales
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 am969@cam.ac.uk Mark Gales Department of Engineering University of Cambridge mjfg@eng.cam.ac.uk |
| 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. |