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DESCRIPTION:In the past decade\, machine learning researchers and theorists have create
 d deep learning architectures which seem to learn complex topics with littl
 e intervention. Newer research in adversarial learning questions just how m
 uch “learning" these networks are doing. Several theories have arisen reg
 arding neural network “blind spots” which can be exploited to fool the 
 network. For example\, by changing a series of pixels which are imperceptib
 le to the human eye\, you can render an image recognition model useless. Th
 is talk will review the current state of adversarial learning research and 
 showcase some open-source tools to trick the "black box."
DTEND;TZID=Europe/Berlin:20171228T150000
DTSTAMP:20171220T105657Z
DTSTART;TZID=Europe/Berlin:20171228T140000
LAST-MODIFIED:20171220T105651Z
LOCATION:Saal Adams
SEQUENCE:0
SUMMARY:Deep Learning Blindspots - Katharine Jarmul
TRANSP:OPAQUE
UID:aed02b65-9a16-4a42-ac28-2883da680d0a@frab.cccv.de
URL;VALUE=URI:https://fahrplan.events.ccc.de/congress/2017/Fahrplan/events/8860.html
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