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].
Deep Momentum Multi-Marginal Schrödinger Bridge
Authors: Tianrong Chen, Guan-Horng Liu, Molei Tao, Evangelos Theodorou
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test DMSB on 2D synthetic datasets and real-world sc RNA-seq dataset [30]. We choose state of the art algorithms MIOFlow [9] and NLSB [11] as our baselines. Our algorithm outperforms baselines significantly, as evidenced by experiments for synthetic datasets and a real-world single-cell RNA sequence dataset. |
| Researcher Affiliation | Academia | Tianrong Chen, Guan-horng Liu, Molei Tao, Evangelos A. Theodorou Georgia Institute of Technology, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Sampling Procedure of DMSB, Algorithm 2 Langevin Sampler at ti marginal constraint, Algorithm 3 DMSB Training, Algorithm 4 Function Opt Sub Set (Optimization for subsets) |
| Open Source Code | No | The paper does not explicitly state that its code is open-source or provide a link to a code repository. It mentions using 'official implementation' for baselines but not for its own work. |
| Open Datasets | Yes | We test DMSB on 2D synthetic datasets and real-world sc RNA-seq dataset [30]. ... We inherit processed data from [8]. |
| Dataset Splits | No | For sc RNA-seq dataset, we split data into train and test subsets(85% and 15%). The paper does not explicitly state a separate validation split percentage. |
| Hardware Specification | Yes | The network adopts position encoding and is trained with Adam W[45] on one Nvidia 3090 Ti GPU. |
| Software Dependencies | No | The paper mentions 'Adam W[45]' but does not specify version numbers for any software libraries or dependencies, such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | The learning rate for all the datasets is set to be 2e-4 and the training batching size is 256. ... The hyperparameters can be found in Table.7. [Referring to Figure 7, which lists 'time steps', '# BI', 'g(t)', '# Parameters', 'T', 'SNR', '# vt Langevin' for different datasets.] |