Optimal transport for single-cell and spatial omics
High-throughput single-cell profiling provides an unprecedented ability to uncover the
molecular states of millions of cells. These technologies are, however, destructive to cells …
molecular states of millions of cells. These technologies are, however, destructive to cells …
[HTML][HTML] How to build the virtual cell with artificial intelligence: Priorities and opportunities
Cells are essential to understanding health and disease, yet traditional models fall short of
modeling and simulating their function and behavior. Advances in AI and omics offer …
modeling and simulating their function and behavior. Advances in AI and omics offer …
A computational framework for solving Wasserstein Lagrangian flows
The dynamical formulation of the optimal transport can be extended through various choices
of the underlying geometry ($\textit {kinetic energy} $), and the regularization of density …
of the underlying geometry ($\textit {kinetic energy} $), and the regularization of density …
Light schr\" odinger bridge
Despite the recent advances in the field of computational Schrodinger Bridges (SB), most
existing SB solvers are still heavy-weighted and require complex optimization of several …
existing SB solvers are still heavy-weighted and require complex optimization of several …
Discrete Diffusion Schr\" odinger Bridge Matching for Graph Transformation
Transporting between arbitrary distributions is a fundamental goal in generative modeling.
Recently proposed diffusion bridge models provide a potential solution, but they rely on a …
Recently proposed diffusion bridge models provide a potential solution, but they rely on a …
Sinkhorn Flow as Mirror Flow: A Continuous-Time Framework for Generalizing the Sinkhorn Algorithm
Many problems in machine learning can be formulated as solving entropy-regularized
optimal transport on the space of probability measures. The canonical approach involves the …
optimal transport on the space of probability measures. The canonical approach involves the …
Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport
Reconstructing dynamics using samples from sparsely time-resolved snapshots is an
important problem in both natural sciences and machine learning. Here, we introduce a new …
important problem in both natural sciences and machine learning. Here, we introduce a new …
Sinkhorn Flow: A Continuous-Time Framework for Understanding and Generalizing the Sinkhorn Algorithm
Many problems in machine learning can be formulated as solving entropy-regularized
optimal transport on the space of probability measures. The canonical approach involves the …
optimal transport on the space of probability measures. The canonical approach involves the …
Categorical Schr\" odinger Bridge Matching
G Ksenofontov, A Korotin - arxiv preprint arxiv:2502.01416, 2025 - arxiv.org
The Schr\" odinger Bridge (SB) is a powerful framework for solving generative modeling
tasks such as unpaired domain translation. Most SB-related research focuses on continuous …
tasks such as unpaired domain translation. Most SB-related research focuses on continuous …
ARTEMIS integrates autoencoders and schrodinger bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series …
Cellular processes like development, differentiation, and disease progression are highly
complex and dynamic (eg, gene expression). These processes often undergo cell …
complex and dynamic (eg, gene expression). These processes often undergo cell …