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..
Accelerated Flow for Probability Distributions
Authors: Amirhossein Taghvaei, Prashant Mehta
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The algorithm is numerically illustrated and compared with the MCMC and Hamiltonian MCMC algorithms. The numerical algorithm along with the results of numerical experiments appear in Sec. 4. |
| Researcher Affiliation | Academia | 1Department of Mechanical Science and Engineering, Coordinated Science Laboratory, University of Illinois at Urbana Champaign, Urbana, IL, USA. Correspondence to: Amirhossein Taghvaei <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Interacting particle implementation of the accelerated gradient flow |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code or links to a code repository. |
| Open Datasets | No | The paper uses synthetic Gaussian and non-Gaussian examples, defining initial and target distributions. It does not mention using or providing access to any publicly available or open datasets for training. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits. It describes simulations for theoretical models rather than experiments on partitioned datasets. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processors, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components, libraries, or solvers used in the implementation. |
| Experiment Setup | Yes | For this simulation, the numerical parameters are as follows: N = 100, φ0(x) = 0.5(x 2), t0 = 1, t = 0.1, p = 2,C = 0.625, and K = 400. The numerical parameters are same as in the Example 4.1. |