Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Blurring Diffusion Models
Authors: Emiel Hoogeboom, Tim Salimans
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 18,476 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,529 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |