Aligned Diffusion Schrödinger Bridges

Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, Maria Rodriguez Martinez, Andreas Krause +1 more
2/22/2023
cs.LGq-bio.QM

No official code found. Best implementation: vsomnath/aligned_diffusion_bridges (33 stars, MIT, last commit 2023-10-01). 1 more below.

Abstract

Diffusion Schrödinger bridges (DSB) have recently emerged as a powerful framework for recovering stochastic dynamics via their marginal observations at different time points. Despite numerous successful applications, existing algorithms for solving DSBs have so far failed to utilize the structure of aligned data, which naturally arises in many biological phenomena. In this paper, we propose a novel algorithmic framework that, for the first time, solves DSBs while respecting the data alignment. Our approach hinges on a combination of two decades-old ideas: The classical Schrödinger bridge theory and Doob's $h$-transform. Compared to prior methods, our approach leads to a simpler training procedure with lower variance, which we further augment with principled regularization schemes. This ultimately leads to sizeable improvements across experiments on synthetic and real data, including the tasks of predicting conformational changes in proteins and temporal evolution of cellular differentiation processes.

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Code Implementations(2)

MIT

Aligned Diffusion Schroedinger Bridges (UAI 2023)

331Shell, PythonJun 21, 20233 years agoMIT
diffusion-modelsgeometrymachine-learningprotein-conformational-changesuai-2023
MIT

Aligned Diffusion Schroedinger Bridges (UAI 2023)

143Shell, PythonJun 21, 20231 years agoMIT

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Cite this paper

@article{somnath2023aligned,
  title  = {Aligned Diffusion Schrödinger Bridges},
  author = {Vignesh Ram Somnath and Matteo Pariset and Ya-Ping Hsieh and Maria Rodriguez Martinez and Andreas Krause and Charlotte Bunne},
  year   = {2023},
  eprint = {2302.11419},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.11419v3}
}

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