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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Scalable Inference in SDEs by Direct Matching of the Fokker–Planck–Kolmogorov Equation
Authors: Arno Solin, Ella Tamir, Prakhar Verma
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The goals of the experiments are three-fold: We first provide a study of the computational complexity. Then, we look into properties of the GP-SDE model from Sec. 2.1, where the experiments are concerned with showcasing model specification rather than inference. Finally, we consider two benchmark problems with high-dimensional inputs for learning a latent SDE model, where we test the performance of the approximations presented when the model is not defined by GPs, as the SDE methods presented in Sec. 2 are model-agnostic. |
| Researcher Affiliation | Collaboration | Arno Solin Aalto University Espoo, Finland EMAIL Ella Tamir Aalto University Espoo, Finland EMAIL Prakhar Verma Aalto University Espoo, Finland EMAIL ET has been employed part-time at Sellforte Oy during the project. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes for the methods and experiments in this paper are available at http://github.com/AaltoML/ scalable-inference-in-SDEs. |
| Open Datasets | Yes | Rotating MNIST ([25], available under CC BY-SA 3.0) The CMU walking data set ([1], CMU Mo Cap available under CC BYND 4.0) |
| Dataset Splits | Yes | we model the sequences of a single subject, 35, for which there are 16 train set, three validation set and four test set sequences. |
| Hardware Specification | Yes | GPU: NVIDIA Tesla V100 32 GB with Intel Xeon Gold 6134 3.2 GHz; CPU: Xeon Gold 6248 2.50GHz. |
| Software Dependencies | No | The paper mentions implementing models in Py Torch [33] but does not specify the version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The hyperparameters (ℓ= 0.2, σ2 = 0.1) are the same in each. |