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..
Training Deep Energy-Based Models with f-Divergence Minimization
Authors: Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the superiority of f-EBM over contrastive divergence, as well as the bene๏ฌts of training EBMs using f-divergences other than KL. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stanford University, Stanford, CA 94305 USA. |
| Pseudocode | Yes | Algorithm 1 Single-Step f-EBM. See code implementation in Appendix E.1. |
| Open Source Code | Yes | Our implementation of f-EBM can be found at: https: //github.com/ermongroup/f-EBM |
| Open Datasets | Yes | We conduct experiments with two commonly used image datasets, Celeb A (Liu et al., 2015) and CIFAR-10 (Krizhevsky et al., 2009). |
| Dataset Splits | Yes | We conduct experiments with two commonly used image datasets, Celeb A (Liu et al., 2015) and CIFAR-10 (Krizhevsky et al., 2009). These are standard benchmark datasets with well-defined splits, implicitly covering validation data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version number or any other software dependencies with specific version numbers. |
| Experiment Setup | Yes | Since the performance is sensitive to the model architectures, for fair comparisons, we use the same architecture and training hyper-parameters for f-EBMs and the contrastive divergence baseline (Du & Mordatch, 2019). See Appendix G.9 for more details. |