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

• URL: http://arxiv.org/abs/2503.01375v1
• Date: Mon, 03 Mar 2025 10:17:56 GMT
• Title: Combining Flow Matching and Transformers for Efficient Solution of Bayesian Inverse Problems
• Authors: Daniil Sherki, Ivan Oseledets, Ekaterina Muravleva,
• Abstract summary: 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.
• Score: 3.868222899558346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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