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..
Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
Authors: Guillaume Huguet, Daniel Sumner Magruder, Alexander Tong, Oluwadamilola Fasina, Manik Kuchroo, Guy Wolf, Smita Krishnaswamy
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on simulated data with bifurcations and merges, as well as sc RNA-seq data from embryoid body differentiation, and acute myeloid leukemia treatment. We compare our results to those other methods that preform population flows including Trajectory Net [44] which is based on a CNF that is regularized to achieve efficient paths, and Diffusion Schrödinger s Bridge (DSB) [10] which is an optimal transport framework for generative modeling. The baseline measure in the quantitative results corresponds to the average distance between the previous and next timepoints for a given time t. |
| Researcher Affiliation | Academia | Guillaume Huguet1 D.S. Magruder2 Alexander Tong1 Oluwadamilola Fasina2 Manik Kuchroo2 Guy Wolf1 Smita Krishnaswamy2 1Université de Montréal; Mila Quebec AI Institute 2 Yale University |
| Pseudocode | Yes | We describe the training procedure of the GAE in algorithm 2 in the supplementary material. We describe the overall training procedure of MIOFlow in algorithm 1. |
| Open Source Code | Yes | Code is available here: https://github.com/KrishnaswamyLab/MIOFlow |
| Open Datasets | Yes | Code and links to publicly available data are provided in the supplemental material. We use Dyngen [6] to simulate a sc RNA-seq dataset from a dynamical cellular process. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] All training details are provided in the supplement. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] Compute resources are discussed in the supplement. |
| Software Dependencies | No | In practice, to compute the Wasserstein between discrete distributions, we use the implementation from the library Python Optimal Transport [14]. |
| Experiment Setup | No | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] All training details are provided in the supplement. |