Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Matérn Gaussian Processes on Riemannian Manifolds
Authors: Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth (he/him)
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,193 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,079 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here we showcase two examples to illustrate the theory: dynamical system prediction and sample path visualization. |
| Researcher Affiliation | Academia | Viacheslav Borovitskiy 1,4 Alexander Terenin 2 Peter Mostowsky 1 Marc Peter Deisenroth3 1St. Petersburg State University 2Imperial College London 3University College London 4St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences |
| Pseudocode | No | No structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures) are present in the paper. |
| Open Source Code | Yes | Code available at HTTPS://GITHUB.COM/SPBU-MATH-CS/RIEMANNIAN-GAUSSIAN-PROCESSES and HTTPS://GITHUB.COM/ATERENIN/SPARSEGAUSSIANPROCESSES.JL. |
| Open Datasets | Yes | We take M to be the dragon manifold from the Stanford 3D scanning repository, modified slightly to remove components not connected to the outer surface. |
| Dataset Splits | No | The paper mentions 'training proceeds using mini-batch stochastic variational inference' but does not provide specific dataset split information (e.g., exact percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., 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 software like the 'Firedrake package' and implies the use of Julia (from the .JL extension in the GitHub link), but it does not provide specific version numbers for these or other ancillary software components. |
| Experiment Setup | Yes | Following Hensman et al. [21], training proceeds using mini-batch stochastic variational inference with automatic relevance determination. The full setup is given in Appendix A. |