BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20181221T160727Z
LOCATION:D167/174
DTSTART;TZID=America/Chicago:20181112T091000
DTEND;TZID=America/Chicago:20181112T100000
UID:submissions.supercomputing.org_SC18_sess151_pec258@linklings.com
SUMMARY:Morning Keynote – Azalia Mirhoseini (Google)
DESCRIPTION:Workshop\nDeep Learning, Machine Learning, Workshop Reg Pass\n
 \nMorning Keynote – Azalia Mirhoseini (Google)\n\nMirhoseini\n\nAdvances i
 n computer systems have been key to the success of Machine Learning (ML) i
 n recent years. With the ubiquitous success of ML, it is now time for a ne
 w era where we can transform the way computer systems are built -- with le
 arning. This talk highlights some of the challenges that modern computer s
 ystems are facing, and how we can use machine learning to address them. Sp
 ecifically, it will cover various combinatorial optimization problems that
  appear in computational graph optimizations and then delve into some of o
 ur recent efforts at Google in addressing these problems with deep Reinfor
 cement Learning (RL). Our results show that we can use RL-based techniques
  to optimize such problems without the need to characterize details of the
  target hardware or the computational graph. Instead, RL finds a solution 
 by only incorporating the reward function of interest such as runtime or m
 emory. Using the reward function, RL learns the implicit trade-offs in the
  underlying hardware and can achieve results that outperform traditional o
 ptimization techniques that rely on heuristics.
URL:https://sc18.supercomputing.org/presentation/?id=pec258&sess=sess151
END:VEVENT
END:VCALENDAR