Linearly constrained Gaussian processes
Authors: Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B. Schön
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experimental results |
| Researcher Affiliation | Academia | Carl Jidling Department of Information Technology Uppsala University, Sweden carl.jidling@it.uu.se, Niklas Wahlström Department of Information Technology Uppsala University, Sweden niklas.wahlstrom@it.uu.se, Adrian Wills School of Engineering University of Newcastle, Australia adrian.wills@newcastle.edu.au, Thomas B. Schön Department of Information Technology Uppsala University, Sweden thomas.schon@it.uu.se |
| Pseudocode | Yes | Algorithm 1 Constructing Gx |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit release statement, or code in supplementary materials) for the source code of the methodology described. |
| Open Datasets | No | The paper uses a 'real data set collected in the experiment' and acknowledges its collection, but does not provide specific access information (link, DOI, repository name, formal citation for public access) for this dataset. |
| Dataset Splits | No | The paper mentions '500 train data points and 1 000 test data points' for the real-data experiment and '50 measurements' for the simulated experiment, but it does not specify a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The squared exponential covariance function k(x, x ) = σ2 f exp 1 2l 2 x x 2 has been used for kg and k with hyperparameters chosen by maximizing the marginal likelihood. We have used the value a = 0.01 in (16)., We have used 50 measurements randomly picked over the domain [0 4] [0 4], generated with the noise level σ = 10 4. |