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..
Variational Diffusion Models
Authors: Diederik Kingma, Tim Salimans, Ben Poole, Jonathan Ho
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We introduce a flexible family of diffusion-based generative models that achieve new state-of-the-art log-likelihoods on standard image density estimation benchmarks (CIFAR-10 and Image Net)... We empirically compare to these works, as well as others, in Table 1. |
| Researcher Affiliation | Industry | Google Research |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We demonstrate our proposed class of diffusion models, which we call Variational Diffusion Models (VDMs), on the CIFAR-10 [Krizhevsky et al., 2009] dataset, and the downsampled Image Net [Van Oord et al., 2016, Deng et al., 2009] dataset, where we focus on maximizing likelihood. |
| Dataset Splits | No | The paper mentions training and testing on datasets like CIFAR-10 and ImageNet, but it does not explicitly provide details about specific train/validation/test splits (e.g., percentages or sample counts) needed for reproduction beyond mentioning the 'test set'. |
| Hardware Specification | Yes | Our models are trained on NVIDIA A100 GPUs... |
| Software Dependencies | No | The paper states, 'We implemented our models in JAX [Bradbury et al., 2018] and Haiku [Anonymous, 2021],' but it does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We use an Adam optimizer [Kingma and Ba, 2014] with 10−4 learning rate. We train for 2000K steps with a batch size of 2048. More details of our model specifications are in Appendix B. |