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..
Flows for simultaneous manifold learning and density estimation
Authors: Johann Brehmer, Kyle Cranmer
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a range of experiments we demonstrate how M-flows learn the data manifold and allow for better inference than standard flows in the ambient data space. |
| Researcher Affiliation | Academia | Johann Brehmer and Kyle Cranmer New York University EMAIL, EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | The code used in our study is available at http://github.com/johannbrehmer/manifold-flow. |
| Open Datasets | Yes | We generate these with a Style GAN2 [25] model trained on the FFHQ dataset [26], sampling n of the GAN latent variables while keeping all others fixed. ... In addition, we use the real-world Celeb A-HQ dataset [26]. |
| Dataset Splits | No | The paper mentions training and test data, but does not explicitly provide details for a separate validation split or how it was used beyond general evaluation. |
| Hardware Specification | No | Funding disclosure: This work was supported in part through the NYU IT High Performance Computing resources, services, and staff expertise. (This is a general statement and lacks specific hardware details like GPU/CPU models.) |
| Software Dependencies | Yes | We are grateful to the authors and maintainers of DELPHES 3 [32], ... PYTHIA8 [39], ... PYTORCH [40], PYTORCH-FID [41], SCIKIT-LEARN [42], and SCIPY [43]. |
| Experiment Setup | Yes | All models are based on rational-quadratic neural spline flows [17]. For tabular datasets, we construct transformations f and h by alternating coupling layers with either random permutations or invertible linear transformations, using between 20 and 35 coupling layers depending on the dataset. For image data, f is based on a multi-scale architecture [5] with between 20 and 28 coupling layers across four levels interspersed with actnorm layers and 1 1 convolutions, closely following Refs. [17, 18]. |