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..
Multimarginal Generative Modeling with Stochastic Interpolants
Authors: Michael Samuel Albergo, Nicholas Matthew Boffi, Michael Lindsey, Eric Vanden-Eijnden
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate these capacities with several numerical examples. |
| Researcher Affiliation | Academia | Michael S. Albergo Center for Cosmology and Particle Physics New York University New York, NY 10003, USA EMAIL; Nicholas M. Boffi Courant Institute of Mathematical Sciences New York University New York, NY 10012, USA EMAIL; Michael Lindsey Department of Mathematics University of California, Berkeley Berkeley, CA 94720, USA EMAIL; Eric Vanden-Eijnden Courant Institute of Mathematical Sciences New York University New York, NY 10012, USA EMAIL |
| Pseudocode | Yes | Algorithm 1: Learning each Ëgk |
| Open Source Code | No | The paper does not provide a direct link to a source-code repository or an explicit statement about releasing the code for the described methodology. |
| Open Datasets | Yes | MNIST dataset, AFHQ-2 animal faces dataset (Choi et al., 2020), Oxford flowers dataset (Nilsback & Zisserman, 2006), and Celeb A dataset (Zhang et al., 2019). |
| Dataset Splits | No | The paper does not provide specific details on training, validation, and test dataset splits (e.g., percentages, sample counts, or splitting methodology). |
| Hardware Specification | No | Table 2 mentions '# GPUs 2 8' but does not specify the models or other detailed hardware specifications. |
| Software Dependencies | No | The paper mentions using a 'U-Net architecture' but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | Table 2: Hyperparameters and architecture for image datasets. |