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..
Estimating Barycenters of Distributions with Neural Optimal Transport
Authors: Alexander Kolesov, Petr Mokrov, Igor Udovichenko, Milena Gazdieva, Gudmund Pammer, Evgeny Burnaev, Alexander Korotin
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We showcase its applicability and effectiveness in illustrative scenarios and image data setups. |
| Researcher Affiliation | Academia | 1Skolkovo Institute of Science and Technology, Moscow, Russia 2Department of Mathematics, ETH Z urich, Z urich, Switzerland 3Artificial Intelligence Research Institute, Moscow, Russia. |
| Pseudocode | Yes | Algorithm 1 OT Barycenter via Neural Optimal Transport |
| Open Source Code | Yes | Our source code is available at https://github. com/justkolesov/NOTBarycenters. |
| Open Datasets | Yes | We use images from the MNIST dataset. ... We utilize Ave, celeba! barycenter benchmark as proposed in (Korotin et al., 2022). |
| Dataset Splits | No | The paper mentions hyperparameters like 'batch size' and '# of epochs' in Table 4, but it does not explicitly state specific train/validation/test splits with percentages or sample counts for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models (e.g., 'NVIDIA A100'), CPU types, or cloud computing instance specifications used for the experiments. |
| Software Dependencies | No | The paper mentions using the 'Style GAN2-ada model' from an official repository, but it does not specify software dependencies with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9') used for their implementation. |
| Experiment Setup | Yes | We aggregate the hyper-parameters of our Algorithm 1 for different experiments in Table 4. |