The Unreasonable Effectiveness of Deep Evidential Regression
Authors: Nis Meinert, Jakob Gawlikowski, Alexander Lavin
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We detail the theoretical shortcomings and analyze the performance on synthetic and realworld data sets, showing that Deep Evidential Regression is a heuristic rather than an exact uncertainty quantification. We go on to discuss corrections and redefinitions of how aleatoric and epistemic uncertainties should be extracted from NNs. |
| Researcher Affiliation | Collaboration | 1Pasteur Labs, 19 Morris Avenue, Brooklyn Navy Yard Building 128, Brooklyn, NY 11205, USA 2German Aerospace Center, Institute of Data Science, M alzerstraße 3-5, 07745 Jena, Germany |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | An open-source Git Hub repository (MIT License) with the Appendix and source code for reproducing our experiments (implementations of the NNs and algorithms to generate the data) is available on https://github.com/pasteurlabs/unreasonable effective der. |
| Open Datasets | Yes | The training set consists of over 27k RGB-to-depth image pairs of indoor scenes from the NYU Depth v2 dataset (Silberman et al. 2012). |
| Dataset Splits | No | The paper mentions training data and testing, but does not provide specific details on validation dataset splits (e.g., percentages or exact counts for a validation set). |
| Hardware Specification | Yes | The model and experiments are lightweight, running locally on a 4-core Mac Book Pro in under an hour. |
| Software Dependencies | No | The paper mentions using 'scripts' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The NN is trained with λ = 0.01 and the Adam optimizer with an initial learning rate of 5 10 4 for 500 epochs. |