Neural Guided Diffusion Bridges

• URL: http://arxiv.org/abs/2502.11909v2
• Date: Tue, 18 Feb 2025 08:49:41 GMT
• Title: Neural Guided Diffusion Bridges
• Authors: Gefan Yang, Frank van der Meulen, Stefan Sommer,
• Abstract summary: We propose a novel method for simulating conditioned diffusion processes (diffusion bridges) in Euclidean spaces. By training a neural network to approximate bridge dynamics, our approach eliminates the need for computationally intensive Markov Chain Monte Carlo (MCMC) methods.
• Score: 2.048226951354646
• License:
• Abstract: We propose a novel method for simulating conditioned diffusion processes (diffusion bridges) in Euclidean spaces. By training a neural network to approximate bridge dynamics, our approach eliminates the need for computationally intensive Markov Chain Monte Carlo (MCMC) methods or reverse-process modeling. Compared to existing methods, it offers greater robustness across various diffusion specifications and conditioning scenarios. This applies in particular to rare events and multimodal distributions, which pose challenges for score-learning- and MCMC-based approaches. We propose a flexible variational family for approximating the diffusion bridge path measure which is partially specified by a neural network. Once trained, it enables efficient independent sampling at a cost comparable to sampling the unconditioned (forward) process.

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