Accelerated Flow for Probability Distributions
Authors: Amirhossein Taghvaei, Prashant Mehta
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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 <taghvae2@illinois.edu>. |
| 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. |