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math.NA2024
A Guide to Stochastic Optimisation for Large-Scale Inverse Problems
Matthias J. Ehrhardt, Zeljko Kereta, Jingwei Liang +1
Stochastic optimisation algorithms are the de facto standard for machine learning with large amounts of data. Handling only a subset of available data in each optimisation step dra…
math.NA2023
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems
Ziruo Cai, Junqi Tang, Subhadip Mukherjee +3
Bayesian methods for solving inverse problems are a powerful alternative to classical methods since the Bayesian approach offers the ability to quantify the uncertainty in the solu…