47 lines
4.9 KiB
BibTeX
47 lines
4.9 KiB
BibTeX
@article{mazeletUnsupervisedLearningOptimal,
|
|
title = {Unsupervised {{Learning}} for {{Optimal Transport}} Plan Prediction between Unbalanced Graphs},
|
|
author = {Mazelet, Sonia and Flamary, Rémi and Thirion, Bertrand},
|
|
abstract = {Optimal transport between graphs, based on Gromov-Wasserstein and other extensions, is a powerful tool for comparing and aligning graph structures. However, solving the associated non-convex optimization problems is computationally expensive, which limits the scalability of these methods to large graphs. In this work, we present Unbalanced Learning of Optimal Transport (ULOT), a deep learning method that predicts optimal transport plans between two graphs. Our method is trained by minimizing the fused unbalanced Gromov-Wasserstein (FUGW) loss. We propose a novel neural architecture with cross-attention that is conditioned on the FUGW tradeoff hyperparameters. We evaluate ULOT on synthetic stochastic block model (SBM) graphs and on real cortical surface data obtained from fMRI. ULOT predicts transport plans with competitive loss up to two orders of magnitude faster than classical solvers. Furthermore, the predicted plan can be used as a warm start for classical solvers to accelerate their convergence. Finally, the predicted transport plan is fully differentiable with respect to the graph inputs and FUGW hyperparameters, enabling the optimization of functionals of the ULOT plan.},
|
|
langid = {english},
|
|
keywords = {/unread},
|
|
annotation = {Read\_Status: New\\
|
|
Read\_Status\_Date: 2025-06-11T09:08:09.864Z},
|
|
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/HPZEYMM9/Mazelet et al. - Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs.pdf}
|
|
}
|
|
|
|
@article{nennaLecture2Entropic,
|
|
title = {Lecture 2: {{Entropic Optimal Transport}}},
|
|
author = {Nenna, Luca},
|
|
langid = {english},
|
|
keywords = {/unread},
|
|
annotation = {Read\_Status: New\\
|
|
Read\_Status\_Date: 2025-06-11T16:06:28.547Z},
|
|
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/WGFIISDB/Nenna - Lecture 2 Entropic Optimal Transport.pdf}
|
|
}
|
|
|
|
@article{nennaLecture1Monge,
|
|
title = {Lecture 1 {{Monge}} and {{Kantorovich}} Problems: From Primal to Dual},
|
|
author = {Nenna, Luca},
|
|
langid = {english},
|
|
keywords = {/unread},
|
|
annotation = {Read\_Status: New\\
|
|
Read\_Status\_Date: 2025-06-13T09:24:13.832Z},
|
|
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/7LVQPD6D/Nenna - Lecture 1 Monge and Kantorovich problems from primal to dual.pdf}
|
|
}
|
|
|
|
@article{Morton2021.11.09.467939,
|
|
title = {Scalable Estimation of Microbial Co-Occurrence Networks with {{Variational Autoencoders}}},
|
|
author = {Morton, James T. and Silverman, Justin and Tikhonov, Gleb and Lähdesmäki, Harri and Bonneau, Rich},
|
|
date = {2021},
|
|
journaltitle = {bioRxiv : the preprint server for biology},
|
|
shortjournal = {bioRxiv},
|
|
eprint = {https://www.biorxiv.org/content/early/2021/11/11/2021.11.09.467939.full.pdf},
|
|
publisher = {Cold Spring Harbor Laboratory},
|
|
doi = {10.1101/2021.11.09.467939},
|
|
url = {https://www.biorxiv.org/content/early/2021/11/11/2021.11.09.467939},
|
|
abstract = {Estimating microbe-microbe interactions is critical for understanding the ecological laws governing microbial communities. Rapidly decreasing sequencing costs have promised new opportunities to estimate microbe-microbe interactions across thousands of uncultured, unknown microbes. However, typical microbiome datasets are very high dimensional and accurate estimation of microbial correlations requires tens of thousands of samples, exceeding the computational capabilities of existing methodologies. Furthermore, the vast majority of microbiome studies collect compositional metagenomics data which enforces a negative bias when computing microbe-microbe correlations. The Multinomial Logistic Normal (MLN) distribution has been shown to be effective at inferring microbe-microbe correlations, however scalable Bayesian inference of these distributions has remained elusive. Here, we show that carefully constructed Variational Autoencoders (VAEs) augmented with the Isometric Log-ratio (ILR) transform can estimate low-rank MLN distributions thousands of times faster than existing methods. These VAEs can be trained on tens of thousands of samples, enabling co-occurrence inference across tens of thousands of microbes without regularization. The latent embedding distances computed from these VAEs are competitive with existing beta-diversity methods across a variety of mouse and human microbiome classification and regression tasks, with notable improvements on longitudinal studies.Competing Interest StatementThe authors have declared no competing interest.},
|
|
elocation-id = {2021.11.09.467939},
|
|
keywords = {/unread},
|
|
annotation = {Read\_Status: New\\
|
|
Read\_Status\_Date: 2025-06-30T14:17:29.518Z}
|
|
}
|