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BEGIN:VEVENT
UID:9b563644b4ef41f0c944a399e9aa0b3f
CATEGORIES:Colloquia
CREATED:20211104T113143
SUMMARY:A new tensor framework - theory and applications
LOCATION:zoom
DESCRIPTION:<p style="background: white;">Abstract:</p><p style="background: white;">Te
 nsors (aka multiway arrays) can be instrumental in revealing latent correla
 tions residing in high dimensional spaces. Despite their applicability to a
  broad range of applications in machine learning, data compression, and ima
 ging, inconsistencies between tensor and matrix algebra have been complicat
 ing their broader utility.&nbsp; Researchers seeking to overcome those disc
 repancies have introduced several different candidate extensions, each intr
 oducing unique advantages and challenges. In this talk, we review some of t
 he common tensor definitions, discuss their limitations, and introduce our 
 tensor product framework which permits the elegant extension of linear alge
 braic concepts and algorithms to tensors.&nbsp; Following introduction of f
 undamental tensor operations, we discuss tensor decompositions in further d
 epth, including tensor SVDs, based on the tensor product framework which ca
 n be computed efficiently in parallel.&nbsp; We present theoretical results
  regarding compressibility and provide demonstrations of the applicability 
 of our theory and algorithms on examples such as model reduction and image 
 representation.&nbsp;&nbsp;</p>
CONTACT:Misha Kilmer (Tufts University)
DTSTAMP:20260830T052023
DTSTART;TZID=America/New_York:20211110T153000
DTEND;TZID=America/New_York:20211110T163000
SEQUENCE:0
TRANSP:OPAQUE
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