Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Light Schrödinger Bridge
Authors: Alexander Korotin, Nikita Gushchin, Evgeny Burnaev
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 34,722 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,426 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |