Kernkonzepte
The authors introduce [SF]2M, a simulation-free objective for inferring stochastic dynamics efficiently. By leveraging entropic optimal transport and neural networks, [SF]2M outperforms existing methods in modeling Schrödinger bridges accurately.
Zusammenfassung
The content discusses the development of a novel method, [SF]2M, for simulating stochastic dynamics without the need for simulations. The approach is compared to existing algorithms in terms of generative modeling performance and single-cell dynamics interpolation. Key concepts include Schrödinger bridge approximation, learning cell dynamics, and gene regulatory network modeling.
The authors present [SF]2M as an efficient solution for inferring stochastic dynamics without simulation-based training objectives. The method is evaluated against other algorithms in various experiments to showcase its effectiveness in different scenarios.
Key points from the content include:
- Introduction of [SF]2M as a simulation-free objective for inferring stochastic dynamics.
- Comparison with existing methods such as DSB, DSBM, OT-CFM, RF, FM, NLSB, TrajectoryNet.
- Evaluation of [SF]2M's performance in generative modeling and single-cell dynamics interpolation.
- Application of [SF]2M in learning gene regulatory networks and modeling cell dynamics accurately.
The study demonstrates the superiority of [SF]2M over traditional methods by showcasing its efficiency and accuracy in various experiments.
Statistiken
Our code is available at https://github.com/atong01/conditional-flow-matching.
The Sinkhorn algorithm is used to compute entropic OT plans efficiently.
Empirical distributions are used to approximate true entropic OT plans due to unknown real distributions.
Marginal ODE drifts and scores are approximated using neural networks vθ(·, ·) and sθ(·, ·).
Zitate
"We find that [SF]2M is more efficient and gives more accurate solutions to the SB problem than simulation-based methods from prior work."
"Unlike with static optimal transport, we are able to directly model and recover the gene-gene interaction network driving the cell dynamics."