Multimarginal Generative Modeling with Stochastic Interpolants
Authors: Michael Samuel Albergo, Nicholas Matthew Boffi, Michael Lindsey, Eric Vanden-Eijnden
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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 albergo@nyu.edu; Nicholas M. Boffi Courant Institute of Mathematical Sciences New York University New York, NY 10012, USA boffi@cims.nyu.edu; Michael Lindsey Department of Mathematics University of California, Berkeley Berkeley, CA 94720, USA lindsey@math.berkeley.edu; Eric Vanden-Eijnden Courant Institute of Mathematical Sciences New York University New York, NY 10012, USA eve2@cims.nyu.edu |
| Pseudocode | Yes | Algorithm 1: Learning each ˆgk |
| 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. |