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..
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Authors: Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, Surya Ganguli
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the utility of these diffusion probabilistic models by training high log likelihood models for a two-dimensional swiss roll, binary sequence, handwritten digit (MNIST), and several natural image (CIFAR-10, bark, and dead leaves) datasets. ... 3. Experiments We train diffusion probabilistic models on a variety of continuous datasets, and a binary dataset. We then demonstrate sampling from the trained model and inpainting of missing data, and compare model performance against other techniques. |
| Researcher Affiliation | Academia | Jascha Sohl-Dickstein EMAIL Stanford University Eric A. Weiss EMAIL University of California, Berkeley Niru Maheswaranathan EMAIL Stanford University Surya Ganguli EMAIL Stanford University |
| Pseudocode | No | No structured pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | We additionally release an open source reference implementation of the algorithm. ... A reference implementation of the algorithm utilizing Blocks (van Merri enboer et al., 2015) is available at https://github.com/Sohl-Dickstein/Diffusion-Probabilistic-Models. |
| Open Datasets | Yes | We demonstrate the utility of these diffusion probabilistic models by training high log likelihood models for a two-dimensional swiss roll, binary sequence, handwritten digit (MNIST), and several natural image (CIFAR-10, bark, and dead leaves) datasets. ... CIFAR-10 (Krizhevsky & Hinton, 2009) dataset. ... MNIST digits (Le Cun & Cortes, 1998). ... Dead leaf images (Jeulin, 1997; Lee et al., 2001). ... bark texture images (T01-T04) from (Lazebnik et al., 2005). |
| Dataset Splits | No | The lower bound K on the log likelihood, computed on a holdout set, for each of the trained models. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are provided for the experimental setup. |
| Software Dependencies | No | In all cases the objective function and gradient were computed using Theano (Bergstra & Breuleux, 2010), and model training was with SFO (Sohl-Dickstein et al., 2014). A reference implementation of the algorithm utilizing Blocks (van Merri enboer et al., 2015) is available at... |
| Experiment Setup | Yes | For all results in this paper, multi-layer perceptrons are used to deο¬ne these functions. ... The multi-scale convolutional architecture shared by these experiments is described in Appendix Section D.2.1, and illustrated in Figure D.1. |