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 [1].
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. |