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What and Who
Title:Efficient Optimization for Very Large Combinatorial Problems in Computer Vision and Machine Learning
Speaker:Paul Swoboda
coming from:Max-Planck-Institut für Informatik - D2
Speakers Bio:
Event Type:Joint Lecture Series
Visibility:D1, D2, D3, INET, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Date, Time and Location
Date:Wednesday, 2 October 2019
Duration:60 Minutes
Building:E1 5
In computer vision and machine learning combinatorial optimization
problems are widespread, typically NP-hard and tend to pose unique
challenges due to their very large scale and problem structure.
Established techniques from the mathematical optimization community
cannot cope with the encountered problem sizes and do not exploit
special problem characteristics. In this talk I will present several
new solution paradigms for solving large scale combinatorial problems
in computer vision efficiently and to high accuracy. I will discuss
how these principles can be applied on classical problems of
combinatorial optimization that have found wide use in computer
vision, machine learning and computer graphics, namely inference in
Markov Random Fields, the quadratic assignment problem and graph
decomposition. Lastly, I will show empirical results showing the great
practical performance of the presented techniques.
Name(s):Jennifer Müller
EMail:--email address not disclosed on the web
Video Broadcast
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Attachments, File(s):
  • Jennifer Müller, 09/09/2019 02:25 PM
  • Jennifer Müller, 08/26/2019 11:49 AM -- Created document.