Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model
Authors: Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, Gregory Wornell
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our proposed meta-model approach s flexibility and superior empirical performance on these applications over multiple representative image classification benchmarks. |
| Researcher Affiliation | Collaboration | Maohao Shen1, Yuheng Bu2, Prasanna Sattigeri3, Soumya Ghosh3, Subhro Das3, Gregory Wornell1 1 Massachusetts Institute of Technology 2 University of Florida 3 MIT-IBM Watson AI Lab, IBM Research maohao@mit.edu |
| Pseudocode | No | The paper describes methods through text and mathematical equations, but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | For both OOD detection and misclassification tasks, we employ three standard datasets to train the base model and the meta-model: MNIST, CIFAR10, and CIFAR100. |
| Dataset Splits | Yes | Validation with early stopping is a commonly used technique in supervised learning to train a model with desired generalization performance, i.e., stop training when the error evaluated on the validation set starts increasing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory, or specific cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies or libraries used in the implementation or experimentation. |
| Experiment Setup | No | The paper describes the base model structures and general meta-model architecture but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations in the main text. It mentions 'More experiment results and implementation details are given in the Appendix B and Appendix C,' indicating such details are external to the main body. |