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].
Light Unbalanced Optimal Transport
Authors: Milena Gazdieva, Arip Asadulaev, Evgeny Burnaev, Aleksandr Korotin
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide the generalization bounds for our solver (M4.4) and experimentally test it on several tasks (M5.1, M5.2). ... In this section, we test our U-Light OT solver on several setups from the related works. |
| Researcher Affiliation | Academia | Milena Gazdieva Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia EMAIL Arip Asadulaev ITMO University Artificial Intelligence Research Institute Moscow, Russia EMAIL Evgeny Burnaev Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia EMAIL Alexander Korotin Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow, Russia EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The code is publicly available at https://github.com/milenagazdieva/Light Unbalanced Optimal Transport |
| Open Datasets | Yes | We follow the setup of [41, Section 5.4] and use pre-trained ALAE autoencoder [55] for 1024 1024 FFHQ dataset [34] of human faces. |
| Dataset Splits | No | The paper mentions 'Number of train FFHQ images for each subset' in Table 2, but does not explicitly state specific train/validation/test dataset splits (e.g., percentages or sample counts for each split) for its experiments. |
| Hardware Specification | Yes | Each experiment requires several minutes of training on CPU with 4 cores. |
| Software Dependencies | No | The code is written using Py Torch framework and is publicly available at... We use the Adam optimizer... To obtain an optimal transport plan π (x, y) discrete OT solvers from the POT [21] package were used. |
| Experiment Setup | Yes | We use K = L = 5, ε = 0.05, lr = 3e 4 and batchsize 128. We do 2 104 gradient steps... for our solver, we use weighted DKL divergence with parameters τ specified in Appendix C, and set K = L = 10, ε = 0.05, lr = 1, and batch size to 128. We do 5 103 gradient steps using Adam optimizer [35] and Multi Step LR scheduler with parameter γ = 0.1 and milestones= [500, 1000]. |