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..
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
Authors: Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the validity of our theory on both synthetic data and neural networks. ... We support our theory with experiments that are conducted on both synthetic and real data. |
| Researcher Affiliation | Academia | 1: LTCI, Télécom Paris, Institut Polytechnique de Paris, France 2: CMLA, ENS Cachan, CNRS, Université Paris-Saclay, France 3: Department of Statistics, University of Oxford, UK |
| Pseudocode | No | The paper does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | We provide the code to reproduce the experiments.2 ... 2See https://github.com/kimiandj/min_swe. |
| Open Datasets | Yes | We use the MNIST dataset, made of 60 000 training images and 10 000 test images of size 28 28. |
| Dataset Splits | No | The paper mentions 60,000 training images and 10,000 test images for the MNIST dataset but does not specify a validation split or percentages for data partitioning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions using the ADAM optimizer [32] but does not provide specific version numbers for any software components or libraries, which is required for reproducibility. |
| Experiment Setup | Yes | We trained for 20 000 iterations with the ADAM optimizer [32]. Our training objective is MESWE of order 2 approximated with 20 random projections and 20 different generated datasets. We design a neural network with the fully-connected configuration given in [16, Appendix D]. |