Reliable training and estimation of variance networks
Authors: Nicki Skafte, Martin Jørgensen, Søren Hauberg
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we investigate the impact of predictive uncertainty on multiple datasets and tasks ranging from regression, active learning and generative modeling. |
| Researcher Affiliation | Academia | Nicki S. Detlefsen nsde@dtu.dk Martin Jørgensen* marjor@dtu.dk Søren Hauberg sohau@dtu.dk *Equal contribution Section for Cognitive Systems, Technical University of Denmark |
| Pseudocode | Yes | Pseudo-code of this sampling-scheme, can be found in the supplementary material. |
| Open Source Code | Yes | Implementation details and code can be found in the supplementary material. |
| Open Datasets | Yes | More precisely, we consider weather data from over 130 years.7 Each day the maximum temperature is measured... 7https://mrcc.illinois.edu/CLIMATE/Station/Daily/Stn Dy BTD2.jsp ...we experimented with four UCI regression datasets (Fig. 5). ... For our last set of experiments we fitted a standard VAE and our Comb-VAE to four datasets: MNIST, Fashion MNIST, CIFAR10, SVHN. |
| Dataset Splits | No | The paper specifies a '20% train, 60% pool and 20% test' split for active learning, but does not explicitly mention a separate validation set split in the main text. |
| Hardware Specification | No | The paper states: 'We gratefully acknowledge the support of NVIDIA Corporation with the donation of GPU hardware used for this research.' but does not specify the model or type of GPU. |
| Software Dependencies | No | The paper mentions 'Implementation details and code can be found in the supplementary material,' but does not provide specific software names with version numbers in the main text. |
| Experiment Setup | No | The details about network architecture and training can be found in the supplementary material. |