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 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. |