Linearly constrained Gaussian processes
Authors: Carl Jidling, Niklas Wahlström, Adrian Wills, Thomas B. Schön
NeurIPS 2017 | Venue 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 EMAIL, Niklas Wahlström Department of Information Technology Uppsala University, Sweden EMAIL, Adrian Wills School of Engineering University of Newcastle, Australia EMAIL, Thomas B. Schön Department of Information Technology Uppsala University, Sweden EMAIL |
| 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. |