結合流匹配與變換器以高效求解貝葉斯逆問題

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

高效解決貝葉斯逆問題仍然是一個重大挑戰，這源於後驗分佈的複雜性以及傳統抽樣方法的計算成本。給定一系列觀測數據和前向模型，我們希望恢復在觀測實驗數據條件下的參數分佈。我們展示，通過將條件流匹配（CFM）與基於變壓器的架構相結合，我們能夠高效地從這類分佈中抽樣，並適應可變數量的觀測條件。

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.