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PRODID:-//Cornell U. Department of Computer Science//Brown Bag Seminar//EN
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SUMMARY:Brown bag: Christopher De Sa
DESCRIPTION:Title: Accelerating Machine Learning with Fast Stochastic
	 Algorithms\nSpeaker: Christopher De Sa\nAbstract: As machine learning
	 applications become larger and more widely used\, there is an increasing
	 need for efficient systems solutions. The performance of essentially all
	 machine learning applications is limited by bottlenecks\, such as
	 parallelizability and memory bandwidth\, with effects that cut across
	 traditional layers in the software stack. The key property that helps us
	 address these bottlenecks is the fact that machine learning problems are
	 statistical and thus have some built-in error tolerance: this gives us
	 additional degrees of freedom that we can use when designing and
	 optimizing machine learning algorithms.  To use these extra degrees of
	 freedom effectively\, we need to develop techniques that can leverage
	 noise-tolerance to increase the throughput of our systems\, while
	 provably having little effect on their accuracy.\n\n \n\nIn practice\,
	 there is a broad class of algorithms\, stochastic iterative algorithms\,
	 that often determine the performance of machine learning systems. In
	 this talk\, I will describe several methods that can be applied to speed
	 up stochastic iterative algorithms in a principled way by using
	 high-level structural information about a problem. I will also discuss
	 future research directions\, including a new approach to getting highly
	 accurate solutions while mostly using energy-efficient low-precision
	 computation.
LOCATION:Gates 122
UID:2017-10-17
STATUS:CONFIRMED
DTSTART:20171017T160000Z
DTEND:20171017T170000Z
LAST-MODIFIED:20171016T135025Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260408T121740Z
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