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What and Who
Title:Inspectable Deep Learning with Bayesian Statistics
Speaker:Max Maria Losch
coming from:Max-Planck-Institut für Informatik - D2
Speakers Bio:PhD student
Event Type:IMPRS Research Seminar
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Date, Time and Location
Date:Monday, 16 July 2018
Duration:30 Minutes
Building:E1 4
As deep neural networks are starting to be employed in safety critical applications like autonomous driving, there is a rising interest in making their inner workings more interpretable to allow inspection of failure cases. The complexity and opacity of these networks can make it difficult to explain why the network made a particular prediction. In this talk, I will present our ongoing work on how we address this issue via lifting the anonymity of features by quantifying their discriminativity for high-level concepts or classes and how we can utilize this information to approach more inspectable deep neural networks.
Name(s):IMPRS-CS Office
Phone:0681 93251800
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:NoTo Location:
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Attachments, File(s):
Stephanie Jörg/MPI-INF, 07/13/2018 09:37 AM
Last modified:
Uwe Brahm/MPII/DE, 07/16/2018 07:01 AM
  • Stephanie Jörg, 07/13/2018 09:43 AM -- Created document.