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METHOD:PUBLISH
UID:4f7c9f86-03bd-4ac9-9210-7e284b850ac7
X-WR-CALNAME:Workshop « Explainable Deep learning/AI » on congress IAPR IEE
 E ICPR’2021
X-WR-TIMEZONE:Europe/Paris
BEGIN:VTIMEZONE
TZID:Europe/Paris
TZUNTIL:20221030T010000Z
BEGIN:STANDARD
TZNAME:CET
DTSTART:20201025T030000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
RDATE:20211031T030000
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TZNAME:CEST
DTSTART:20200329T020000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
RDATE:20210328T020000
RDATE:20220327T020000
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BEGIN:VEVENT
UID:4f7c9f86-03bd-4ac9-9210-7e284b850ac7
DTSTAMP:20260420T195609Z
CLASS:PUBLIC
DESCRIPTION:Jenny Benois Pineau will facilitate the workshop « Explainable 
 Deep learning/AI » on congress IAPR IEEE ICPR’2021.\nThe recent focus of A
 I and Pattern Recognition communities on the supervised learning approache
 s\, and particularly to Deep Learning / AI\, resulted in considerable incr
 ease of performance of Pattern Recognition and AI systems\, but also raise
 d the question of the trustfulness and explainability of their predictions
  for decision-making. Instead of developing and using Deep Learning as a b
 lack box and adapting known Neural Networks architectures to variety of pr
 oblems\, the goal of explainable Deep Learning / AI is to propose methods 
 to “understand” and “explain” how the these systems produce their decision
 s.\n\n&nbsp\;
DTSTART;TZID=Europe/Paris:20210111T090000
DTEND;TZID=Europe/Paris:20210111T190000
LOCATION:Milan
ORGANIZER;CN=Romain Giot:mailto:romain.giot@u-bordeaux.fr
SEQUENCE:0
SUMMARY:Workshop « Explainable Deep learning/AI » on congress IAPR IEEE ICP
 R’2021
TRANSP:OPAQUE
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