Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes
Authors: Rui Li
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted across domains show superior performances over the state-of-the-art methods. |
| Researcher Affiliation | Academia | Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester, NY 14623 rxlics@rit.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The dataset is available in ftp://msel.mayo.edu/EEG_Data/ and The crime data are available on http://opendata.dc.gov |
| Dataset Splits | Yes | We use 10-fold cross-validation (CV) to evaluate predictions with 30% test set while keeping the same proportion of SOZ and non-SOZ observations in both sets. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For each observation, we simulate 3 chains of 7000 Gibbs iterations, and discard the first 3000 as burn-in phase. Each sampling chain is initialized with parameters sampled from their priors. We set Γ(a, b) prior on the ARD precisions as a = |Sk| and b = a/1000, where Sk is defined in (23). |