BEGIN:VCALENDAR
METHOD:PUBLISH
VERSION:2.0
PRODID:-//Cornell U. Department of Computer Science//Brown Bag Seminar//EN
BEGIN:VEVENT
SUMMARY:Brown bag: Christopher De Sa
DESCRIPTION:Title: Learning in Low Precision Without Losing
	 Accuracy\nSpeaker: Christopher De Sa\nAbstract: Recently there has been
	 an explosion of interest around studying the effects of low-precision
	 computation on machine learning applications. This is because
	 purpose-built\, low-precision hardware accelerators can lower both the
	 time and energy needed to complete a task. Despite this\, the
	 statistical effects of low-precision computation during training are not
	 well understood. This is due to a tradeoff typically found with
	 low-precision training algorithms: as the number of bits is lowered\,
	 noise that limits statistical accuracy is added. How can we avoid the
	 accuracy lost when using low-precision arithmetic for learning? And can
	 this tradeoff be avoided entirely in some cases? In this talk\, I will
	 describe two new training algorithms that address these questions\, both
	 of which use a small amount of infrequent high-precision computation to
	 reduce the error caused by low-precision computation.
LOCATION:Gates 122
UID:2018-10-23
STATUS:TENTATIVE
DTSTART:20181023T160000Z
DTEND:20181023T170000Z
LAST-MODIFIED:20181022T072610Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260408T121801Z
END:VEVENT
END:VCALENDAR