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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Poisson Flow Generative Models
Authors: Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 19,136 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,595 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 | Experimentally, PFGM achieves current state-of-the-art performance among the normalizing flow models on CIFAR-10, with an Inception score of 9.68 and a FID score of 2.35. |
| Researcher Affiliation | Academia | Massachusetts Institute of Technology EMAIL; EMAIL |
| Pseudocode | Yes | Algorithm 1: Learning the normalized Poisson Field |
| Open Source Code | Yes | The code is available at https: //github.com/Newbeeer/poisson_flow. |
| Open Datasets | Yes | For image generation tasks, we consider the CIFAR-10 [22], Celeb A 64 64 [38] and LSUN bedroom 256 256 [39]. |
| Dataset Splits | Yes | We follow the training procedure in [33] and split the training data into 99% training and 1% validation sets for model selection. |
| Hardware Specification | Yes | All the experiments are run on a single NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions 'Scipy library [37] with the RK45 [7] method' but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | We choose M = 291 (CIFAR-10 and Celeb A) 356 (LSUN bedroom), σ = 0.01 and = 0.03 for the perturbation Algorithm 2, and zmin = 1e 3, zmax = 40 (CIFAR-10) 60 (Celeb A 642) 100 (LSUN bedroom) for the backward ODE. We further clip the norms of initial samples into (0,3000) for CIFAR-10, (0,6000) for Celeb A 642 and (0,30000) for LSUN bedroom. |