Unbalanced Sobolev Descent
Authors: Youssef Mroueh, Mattia Rigotti
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show on synthetic examples that USD transports distributions with or without conservation of mass faster than previous particle descent algorithms, and finally demonstrate its use for molecular biology analyses where our method is naturally suited to match developmental stages of populations of differentiating cells based on their single-cell RNA sequencing profile. |
| Researcher Affiliation | Industry | Youssef Mroueh, Mattia Rigotti IBM Research AI mroueh@us.ibm.com, mrg@zurich.ibm.com |
| Pseudocode | Yes | Neural USD with re-weighting is summarized in Algorithm 1 in Appendix B. Note that the re-weighting can also be implemented via a birth-death process as in [12]. We give the details of the implementation as birth-death process in Algorithm 2 (Appendix B). |
| Open Source Code | Yes | Code is available at http://github.com/ibm/usd. |
| Open Datasets | Yes | We test this procedure on the dataset released by [16]. Geoffrey Schiebinger, Jian Shu, Marcin Tabaka, Brian Cleary, Vidya Subramanian, Aryeh Solomon, Joshua Gould, Siyan Liu, Stacie Lin, Peter Berube, et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell, 176(4):928 943, 2019. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions libraries like the POT library and random Fourier features but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | In all our experiments we report the MMD distance with a gaussian kernel, computed using the random Fourier features (RF) approximation [23] with 300 RF and kernel bandwith equal to d (the input dimension). We consider the conservation of mass case, i.e. γ = 1. and The computational complexity Neural USD is given by that of updating the witness function and particles by SGD with backprop, i.e. O(N(T + B)), where N is the mini-batch size, T is the training time, B is the gradient computation time for particles update. |