Estimating Epistemic and Aleatoric Uncertainty with a Single Model
Authors: Matthew Chan, Maria Molina, Chris Metzler
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our method on two distinct real-world tasks: x-ray computed tomography reconstruction and weather temperature forecasting. Source code is publicly available at https://github.com/matthewachan/hyperdm. |
| Researcher Affiliation | Academia | Matthew A. Chan Department of Computer Science University of Maryland College Park, MD 20742 mattchan@umd.edu Maria J. Molina Department of Atmospheric and Oceanic Science University of Maryland College Park, MD 20742 mjmolina@umd.edu Christopher A. Metzler Department of Computer Science University of Maryland College Park, MD 20742 metzler@umd.edu |
| Pseudocode | No | The paper includes diagrams to illustrate concepts (e.g., Figure 1 for Hyper DM framework) but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code is publicly available at https://github.com/matthewachan/hyperdm. |
| Open Datasets | Yes | Using the Lung Nodule Analysis 2016 (LUNA16) [51] dataset, we form a target image distribution X by extracting 1,200 CT images, applying 4 pixel binning to produce 128 128 resolution images, and normalizing each image by mapping pixel values between [ 1000, 3000] Hounsfield units to the interval [ 1, 1]. |
| Dataset Splits | Yes | The dataset is finally split into a training dataset comprised of 1,000 image-measurement pairs and a validation dataset of 200 data pairs. |
| Hardware Specification | Yes | All baselines are trained on a single NVIDIA RTX A6000 using a batch size of 32, an Adam [31] optimizer, and a learning rate of 1 10 4. |
| Software Dependencies | No | The paper mentions using an 'Adam [31] optimizer' and 'Py Torch' (in the NeurIPS checklist justification), but it does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | All baselines are trained on a single NVIDIA RTX A6000 using a batch size of 32, an Adam [31] optimizer, and a learning rate of 1 10 4. Training is run over 500 epochs in our initial experiment and 400 epochs in our CT and weather experiments. DMs are trained using a Markov chain of T = 100 timesteps. |