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..
Light Schrödinger Bridge
Authors: Alexander Korotin, Nikita Gushchin, Evgeny Burnaev
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of our light solver in a series of synthetic and real-data experiments (M5), including the ones with the real biological data (M5.3) considered in related works. |
| Researcher Affiliation | Academia | Alexander Korotin 1,2, Nikita Gushchin 1, Evgeny Burnaev1,2. 1Skolkovo Institute of Science and Technology, 2Artificial Intelligence Research Institute EMAIL, EMAIL |
| Pseudocode | No | The paper describes training and inference procedures in text and with equations, but it does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | The code for our solver can be found at https://github.com/ngushchin/Light SB. |
| Open Datasets | Yes | We use data from the Kaggle competition Open Problems Multimodal Single-Cell Integration : https://www.kaggle.com/competitions/open-problems-multimodal |
| Dataset Splits | No | The paper mentions 'train data' and 'test faces' for specific experiments but does not provide explicit details about training/validation/test splits, such as percentages, counts, or references to predefined validation splits, for all experiments. |
| Hardware Specification | No | The paper states that the solver runs 'on CPU' and specifies '4 CPU cores' but does not provide details on the specific CPU model or processor used. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Adam optimiser' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We use K = 500 in all the cases. For ϵ = 10^-1 and ϵ = 10^-2, we use lr = 10^-3 and for ϵ = 2 * 10^-3 we use lr = 10 and batchsize 128. We do 10^4 gradient steps. |