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CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Research
CATEGORIES:Technology
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=8a0870ee-4cc8788f-014c-db513934-00003e9f:Dawn\, Ari
 el
CREATED:20191009T143821Z
DESCRIPTION:Sparsity-based methods for inverse problems gained widespread 
 popularity in the 2000s when compressed-sensing theory provided groundbre
 aking insights on sparse recovery from randomized measurements. However\,
  this theory does not explain the empirical success of sparse-recovery te
 chniques in problems where the measurements are deterministic and structu
 red. In the first half of this talk we will present a theory of sparse re
 covery for deterministic measurement operators relevant to optics\, elect
 roencephalography\, quantitative magnetic-resonance imaging\, and signal 
 processing. In the second half we will illustrate the potential of deep n
 eural networks in this domain with an application to super-resolution of 
 line spectra. Deep learning techniques achieve remarkable empirical perfo
 rmance for a variety of inverse problems\, but the mechanisms they implem
 ent are shrouded in mystery. We will conclude by showing that a local lin
 ear-algebraic analysis of a network trained for image denoising makes it 
 possible to visualize some of the learned mechanisms\, and reveals intrig
 uing connections to traditional methodology.
DURATION:PT1H
DTSTAMP:20191009T143821Z
DTSTART;TZID=America/New_York:20191022T114500
LAST-MODIFIED:20191009T143821Z
LOCATION;X-BEDEWORK-UID=8a0870ef-50c887f9-0150-ce498495-000037c7:Gross Hal
 l 324
STATUS:CONFIRMED
SUMMARY:Special Seminar in Machine Learning and Optimization Optimization 
 methods for inverse problems: The times they are a-changin
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 _plusDataScience,/principals/users/agrp_BassConnectionsInformation,/princ
 ipals/users/agrp_PrattSchool_BME,/principals/users/agrp_SchoolofMedicine_
 BiostatisticsandBioinformatics,/principals/users/agrp_SchoolofMedicine_CB
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 and Computer Engineering (ECE)\,Information Science + Studies (ISS)\,Math
 ematics\,Pratt School of Engineering\,Social Science Research Institute (
 SSRI)\,Statistical Science
X-BEDEWORK-SPEAKER:Carlos Fernandez-Granda\, Assistant Professor of Mathem
 atics and Data Science
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