结合流匹配与Transformer，高效求解贝叶斯逆问题

Combining Flow Matching and Transformers for Efficient Solution of Bayesian Inverse Problems

March 3, 2025

作者: Daniil Sherki, Ivan Oseledets, Ekaterina Muravleva

cs.AI

摘要

高效解决贝叶斯逆问题仍面临重大挑战，这源于后验分布的复杂性以及传统采样方法的高计算成本。给定一系列观测数据和前向模型，我们的目标是恢复参数在实验观测数据条件下的分布。我们证明，通过将条件流匹配（CFM）与基于Transformer的架构相结合，能够高效地从这类分布中进行采样，且适应于不同数量的观测条件。

English

Solving Bayesian inverse problems efficiently remains a significant challenge due to the complexity of posterior distributions and the computational cost of traditional sampling methods. Given a series of observations and the forward model, we want to recover the distribution of the parameters, conditioned on observed experimental data. We show, that combining Conditional Flow Mathching (CFM) with transformer-based architecture, we can efficiently sample from such kind of distribution, conditioned on variable number of observations.