Seminario del 2025

Giugno
04
2025
Thomas Pock
Prior learning for solving inverse problems
nel ciclo di seminari: PRIOR LEARNING FOR SOLVING INVERSE PROBLEMS
analisi numerica
This teaching unit introduces a computational "toolbox" for learning priors in the context of solving Bayesian inverse problems in imaging. The tools covered include methods for learning optimal discretizations of total-variation related regularization terms, explicit diffusion models based on products of 1D Gaussian mixture models, and the application of the maximum entropy principle for learning generative priors.
organizzato da: INTERNATIONAL PHD SUMMER SCHOOL "MATHEMATICS AND MACHINE LEARNING FOR IMAGE ANALYSIS "
nell'ambito del Progetto Fondi U.E. BPIS_2023_MORIGI Mathematics and Machine Learning for image analysis (ERASMUS+) del prof. Serena Morigi

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