BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
DTSTART:20180311T030000
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:PDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20171105T010000
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:PST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20180503T000000Z
UID:C07F71B5-F79D-48E8-BC8B-2A8BEC58C3D1
DTSTART;TZID=America/Los_Angeles:20180207T183000
DTEND;TZID=America/Los_Angeles:20180207T210000
DESCRIPTION:In recent years\, deep neural networks have found wide ranging 
 applications in the general field of AI. However\, it has been shown that 
 deep learning suffers from Adversarial Examples which have puzzled AI scie
 ntists. For acceptance of deep learning based AI solutions\, it is importa
 nt to understand this intriguing phenomenon and to eliminate it. Furthermo
 re\, there has been debate in social media about urgent need to bring rigo
 r to the field of deep learning. Inspired by that\, in this talk\, we pres
 ent novel results which explain adversarial examples in Computer Vision an
 d which also pave way for future progress in AI.\n\nSpeaker(s): Dr. Simant
  Dube\, \n\nBldg: Intel SC12 Auditorium\, 3600 Juliette Ln\, Santa Clara\,
  California\, United States\, 95054
LOCATION:Bldg: Intel SC12 Auditorium\, 3600 Juliette Ln\, Santa Clara\, Cal
 ifornia\, United States\, 95054
ORGANIZER:paveltc@gmail.com
SEQUENCE:8
SUMMARY:Building Intuition into Adversarial Examples in Deep Learning
URL;VALUE=URI:https://events.vtools.ieee.org/m/158937
X-ALT-DESC:Description: <br /><div>In recent years\, deep neural networks h
 ave found wide ranging applications in the general field of AI. However\, 
 it has been shown that deep learning suffers from Adversarial Examples whi
 ch have puzzled AI scientists. For acceptance of deep learning based AI so
 lutions\, it is important to understand this intriguing phenomenon and to 
 eliminate it. Furthermore\, there has been debate in social media about ur
 gent need to bring rigor to the field of deep learning. Inspired by that\,
  in this talk\, we present novel results which explain adversarial example
 s in Computer Vision and which also pave way for future progress in AI.</d
 iv>
END:VEVENT
END:VCALENDAR