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..
On a Neural Implementation of Brenierโs Polar Factorization
Authors: Nina Vesseron, Marco Cuturi
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments, 5.1. Accuracy Metrics for NPF, Table 1. Polar factorization and Inverse multivalued map metrics for learning the gradient of the elevation in Chamonix area. |
| Researcher Affiliation | Collaboration | 1CREST-ENSAE, IP Paris 2Apple. |
| Pseudocode | Yes | Algorithm 1 Training of Xฯ |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use the Python package elevation to get the elevation of three regions of the world: Chamonix, London, and Cyprus. classify MNIST digits. |
| Dataset Splits | Yes | To assess the quality of our method NPF, we used a 85% training / 15% test split. |
| Hardware Specification | No | This work was performed using HPC resources from GENCI IDRIS (Grant 2023-103245). |
| Software Dependencies | No | The paper mentions software components like 'ADAM solver' and 'diffrax', but does not provide specific version numbers for these or other key software dependencies required for reproducibility. |
| Experiment Setup | Yes | G. Hyperparameters section, specifically Figure 17 (and subsequent figures) which details 'model hyperparameter value' for various components including activation functions, architectures, learning rates, schedulers, and solver parameters. |