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DTSTART;TZID=America/Chicago:20181112T091000
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UID:submissions.supercomputing.org_SC18_sess151_pec258@linklings.com
SUMMARY:Morning Keynote – Azalia Mirhoseini (Google)
DESCRIPTION:Azalia Mirhoseini (Google LLC)\n\nAdvances in computer systems
  have been key to the success of Machine Learning (ML) in recent years. Wi
 th the ubiquitous success of ML, it is now time for a new era where we can
  transform the way computer systems are built -- with learning. This talk 
 highlights some of the challenges that modern computer systems are facing,
  and how we can use machine learning to address them. Specifically, it wil
 l cover various combinatorial optimization problems that appear in computa
 tional graph optimizations and then delve into some of our recent efforts 
 at Google in addressing these problems with deep Reinforcement Learning (R
 L). Our results show that we can use RL-based techniques to optimize such 
 problems without the need to characterize details of the target hardware o
 r the computational graph. Instead, RL finds a solution by only incorporat
 ing the reward function of interest such as runtime or memory. Using the r
 eward function, RL learns the implicit trade-offs in the underlying hardwa
 re and can achieve results that outperform traditional optimization techni
 ques that rely on heuristics.\n\nTag: Deep Learning, Machine Learning\n\nR
 egistration Category: Workshop Reg Pass\n\n
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