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..
Large-Scale Wasserstein Gradient Flows
Authors: Petr Mokrov, Alexander Korotin, Lingxiao Li, Aude Genevay, Justin M. Solomon, Evgeny Burnaev
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate our method on toy and real-world applications. Our code is written in Py Torch and is publicly available at https://github.com/Petr Mokrov/Large-Scale-Wasserstein-Gradient-Flows The experiments are conducted on a GTX 1080Ti. In most cases, we performed several random restarts to obtain mean and variation of the considered metric. As the result, experiments require about 100-150 hours of computation. |
| Researcher Affiliation | Academia | Petr Mokrov Skolkovo Institute of Science and Technology Moscow Institute of Physics and Technology Moscow, Russia EMAIL; Alexander Korotin* Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia EMAIL; Lingxiao Li Massachusetts Institute of Technology Cambridge, Massachusetts, USA EMAIL; Aude Genevay Massachusetts Institute of Technology Cambridge, Massachusetts, USA EMAIL; Justin Solomon Massachusetts Institute of Technology Cambridge, Massachusetts, USA EMAIL; Evgeny Burnaev Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia EMAIL |
| Pseudocode | Yes | Algorithm 1: Fokker-Planck JKO via ICNNs Input :Initial measure ρ0 accessible by samples; JKO discretization step h > 0, number of JKO steps K > 0; target potential Φ(x), diffusion process temperature β 1; batch size N; Output :trained ICNN models {ψ(k)}K k=1 representing JKO steps for k = 1, 2, . . . , K do |
| Open Source Code | Yes | Our code is written in Py Torch and is publicly available at https://github.com/Petr Mokrov/Large-Scale-Wasserstein-Gradient-Flows |
| Open Datasets | Yes | For evaluation, we consider the Bayesian linear regression setup of [42]. We use the 8 datasets from [47]. The number of features ranges from 2 to 60 and the dataset size from 700 to 7400 data points. We also use the Covertype dataset2 with 500K data points and 54 features. 2https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html |
| Dataset Splits | No | We randomly split each dataset into train Strain and test Stest ones with ratio 4:1 and apply the inference on the posterior p(x|Strain). The paper specifies a train/test split ratio (4:1) but does not mention a separate validation split or its details. |
| Hardware Specification | Yes | The experiments are conducted on a GTX 1080Ti. |
| Software Dependencies | No | Our code is written in Py Torch and is publicly available at https://github.com/Petr Mokrov/Large-Scale-Wasserstein-Gradient-Flows. The paper mentions 'Py Torch' but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | In our method, we perform K = 40 JKO steps with step size h = 0.1. We approximate the dynamics of the process by our method with JKO step h = 0.05. In all experiments, we use the Dense ICNN [37, Appendix B.2] architecture for ψθ in Algorithm 1 with Soft Plus activations. |