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SUMMARY:Simone Brugiapaglia (Concordia University)
DTSTART:20260410T223000Z
DTEND:20260410T233000Z
DTSTAMP:20260407T044033Z
UID:AppliedMath/83
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/AppliedMath/
 83/">From compression to depth: generative compressive sensing and deep gr
 eedy unfolding for signal reconstruction</a>\nby Simone Brugiapaglia (Conc
 ordia University) as part of SFU Mathematics of Computation\, Application 
 and Data ("MOCAD") Seminar\n\nLecture held in K9509.\n\nAbstract\nSince it
 s inception in the early 2000s\, compressive sensing has become a well-est
 ablished paradigm for efficient signal recovery\, with applications rangin
 g from medical imaging to scientific computing. More recently\, data-drive
 n reconstruction methods based on deep neural networks have attracted cons
 iderable attention and shown great promise as an alternative approach. In 
 this talk\, we will review recent progress in signal reconstruction techni
 ques that combine principles from compressive sensing and deep learning. F
 irst\, we will discuss recent advances in generative compressive sensing\,
  where the traditional sparsity prior is replaced by the assumption that t
 he signal to be reconstructed lies in the range of a deep generative neura
 l network. Second\, we will explore deep greedy unfolding\, which involves
  designing deep neural network architectures by "unrolling" the iterations
  of a sparse recovery algorithm onto the layers of a trainable neural netw
 ork. In both cases\, we will present numerical results in tandem with theo
 retical guarantees.\n
LOCATION:https://researchseminars.org/talk/AppliedMath/83/
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