Spectral Mixture Kernels for Multi-Output Gaussian Processes
Authors: Gabriel Parra, Felipe Tobar
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method is first validated on synthetic data and then compared to existing MOGP methods on two real-world examples. and We show two sets of experiments. |
| Researcher Affiliation | Academia | Gabriel Parra Department of Mathematical Engineering Universidad de Chile gparra@dim.uchile.cl Felipe Tobar Center for Mathematical Modeling Universidad de Chile ftobar@dim.uchile.cl |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions implementing models in Tensorflow and GPflow, but does not provide concrete access to source code for the MOSM methodology described in this paper. |
| Open Datasets | Yes | The data can be obtained from www.cambermet.co.uk. and the sites therein. and The Jura dataset [3] contains, in addition to other geological data, the concentration of seven heavy metals in a region of 14.5 km2 of the Swiss Jura, and it is divided into a training set (259 locations) and a validation set (100 locations). |
| Dataset Splits | Yes | We chose N1 = 500 samples from the reference function in the interval [-20, 20], N2 = 400 samples from the derivative signal in the interval [-20, 0], and N3 = 400 samples from the delayed signal in the interval [-20, 0]. and We considered the normalised air temperature signal from... from where we randomly chose N = 1000 samples for training. and The Jura dataset [3] contains... divided into a training set (259 locations) and a validation set (100 locations). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Tensorflow [18] using GPflow [19]' but does not provide specific version numbers for these software dependencies, which are required for reproducibility. |
| Experiment Setup | Yes | The NLL is then minimised with respect to Θ = {w(q) i , µ(q) i , Σ(q) i , θ(q) i , φ(q) i , σ2 i,noise}m,Q i=1,q=1, that is, the original parameters chosen to construct R(ω) in Section 3.1, plus the noise hyperparameters. |