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 | Conference PDF | Archive PDF | Plain Text | 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. |