Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Algorithmic Linearly Constrained Gaussian Processes
Authors: Markus Lange-Hegermann
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For the demonstration in Figure 1 we assume squared exponential covariance functions and a zero mean function for four uncorrelated parametrising functions (electric potential and magnetic potentials). For a demonstration of how to observe resp. control such a system see Figures 2 resp. 3. A posterior mean field is demonstrated in Figure 4. |
| Researcher Affiliation | Academia | Markus Lange-Hegermann Department of Electrical Engineering and Computer Science Ostwestfalen-Lippe University of Applied Sciences Lemgo EMAIL |
| Pseudocode | No | The paper includes a snippet of Macaulay2 code in Example 4.3 to demonstrate a computation, but it does not provide general structured pseudocode or algorithm blocks for the proposed algorithmic construction method. |
| Open Source Code | No | The paper mentions and provides links to third-party computer algebra systems (e.g., Macaulay2, Singular) that implement Gröbner bases, but it does not state that the authors' own implementation or code specific to their methodology is available. |
| Open Datasets | No | The paper describes generating specific observation points or conditions for its demonstrations (e.g., "a single observation of electric current", "4 evenly distributed points on the equator") rather than using or providing access information for a publicly available dataset. |
| Dataset Splits | No | The paper does not utilize or describe dataset splits (training, validation, testing) as it focuses on demonstrating concepts with specific, often single-point, observations rather than large-scale data evaluations. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU, memory) used for conducting the computational examples or generating the figures. |
| Software Dependencies | Yes | SINGULAR 4-1-0 A computer algebra system for polynomial computations. http://www.singular.uni-kl.de, 2016. |
| Experiment Setup | Yes | For the demonstration in Figure 1 we assume squared exponential covariance functions and a zero mean function for four uncorrelated parametrising functions (electric potential and magnetic potentials). For a parametrizing functions with squared exponential covariance functions k(t1, t2) = exp( 1 2(t1 t2)2) and a zero mean function... |