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..
Blurring Diffusion Models
Authors: Emiel Hoogeboom, Tim Salimans
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 EXPERIMENTS |
| Researcher Affiliation | Industry | Emiel Hoogeboom Google Research, Brain Team, Amsterdam, Netherlands Tim Salimans Google Research, Brain Team, Amsterdam, Netherlands |
| Pseudocode | Yes | A.1 PSEUDO-CODE OF DIFFUSION AND DENOISING PROCESS |
| Open Source Code | No | An example of a denoising diffusion implementation https://github.com/w86763777/pytorch-ddpm |
| Open Datasets | Yes | CIFAR10 dataset (Krizhevsky et al., 2009) |
| Dataset Splits | No | No explicit mention of a validation dataset split used for training or hyperparameter tuning. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned. |
| Experiment Setup | Yes | All models where optimized with Adam, with a learning rate of 2 10 4 and batch size 128 for CIFAR-10 and a learning rate of 1 10 4 and batch size 256 for the LSUN models. All methods are evaluated with an exponential moving average computed with a decay of 0.9999. |