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..
Revisiting Contrastive Divergence for Density Estimation and Sample Generation
Authors: Azwar Abdulsalam, Joseph G. Makin
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that a simple Conv Net can be trained with this method to be good at generation as well as density estimation for CIFAR-10, Oxford Flowers, and a synthetic dataset in which the learned density can be verified visually. |
| Researcher Affiliation | Academia | Azwar Abdulsalam EMAIL Elmore School of Electrical and Computer Engineering Purdue University Joseph G. Makin EMAIL Elmore School of Electrical and Computer Engineering Purdue University |
| Pseudocode | Yes | Algorithm 1: Hybrid training of EBM |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the release of source code for the methodology described. |
| Open Datasets | Yes | We demonstrate that a simple Conv Net can be trained with this method to be good at generation as well as density estimation for CIFAR-10, Oxford Flowers, and a synthetic dataset in which the learned density can be verified visually. |
| Dataset Splits | Yes | Data-initialized chains are run for L = 10, 000 steps, starting from samples from the test partition of the relevant data sets. All models are initialized at the same test-data samples to facilitate comparisons between them. |
| Hardware Specification | Yes | All models were trained for 10,000 iterations using a single V100 GPU. |
| Software Dependencies | No | The paper mentions using 'scipy' in Section A.1 but does not provide version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | For persistent and persistent+refresh initializations, we apply Langevin dynamics with a step size of ϵ = 0.05 and a temperature (see Section A.4) of T = 0.005. For data and hybrid initializations, we employ an adaptive step size: at each training iteration, the step size is set as ϵ = 0.0005/||E(x)||... All models were trained for 10,000 iterations using a single V100 GPU. |