BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Michael Möller (University of Siegen) DTSTART:20210504T101500Z DTEND:20210504T114500Z DTSTAMP:20260423T022603Z UID:MathDeep/3 DESCRIPTION:Title: On the Confluence of Deep Learning and Energy Minimization Methods for In verse Problems\nby Michael Möller (University of Siegen) as part of M athematics of Deep Learning\n\n\nAbstract\nMany practical applications req uire to infer a desired quantity from measurements that contain implicit i nformation about them\, commonly resulting in ill-posed inverse reconstruc tion problems. While classical approaches formulate their solution as the argument that minimizes a suitable cost function\, recent works dominate i mage reconstruction benchmarks using deep learning. This talk discusses po ssible ways of combining ideas from energy minimization and deep learning\ , including algorithmic schemes that introduce learned regularity\, networ ks that iteratively minimize a model based cost function\, and techniques that aim at learning suitable regularizers. For the latter\, I will highli ght recent advances and future challenges in the design of such parameteri zed regularizers as well as the solution of the bi-level optimization prob lems resulting from their training.\n LOCATION:https://researchseminars.org/talk/MathDeep/3/ END:VEVENT END:VCALENDAR