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 [1].
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. |