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UID:ai1ec-22423@www.clsp.jhu.edu
DTSTAMP:20240329T103657Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:
DTSTART;TZID=America/New_York:20221007T120000
DTEND;TZID=America/New_York:20221007T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Ariya Rastrow (Amazon)
URL:https://www.clsp.jhu.edu/events/ariya-rastrow-amazon-2/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2022\,October\,Rastrow
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-22408@www.clsp.jhu.edu
DTSTAMP:20240329T103657Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:
Abstract
\nAI-powered appl
ications increasingly adopt Deep Neural Networks (DNNs) for solving many p
rediction tasks\, leading to more than one DNNs running on resource-constr
ained devices. Supporting many models simultaneously on a device is challe
nging due to the linearly increased computation\, energy\, and storage cos
ts. An effective approach to address the problem is multi-task learning (M
TL) where a set of tasks are learned jointly to allow some parameter shari
ng among tasks. MTL creates multi-task models based on common DNN architec
tures and has shown significantly reduced inference costs and improved gen
eralization performance in many machine learning applications. In this tal
k\, we will introduce our recent efforts on leveraging MTL to improve accu
racy and efficiency for edge computing. The talk will introduce multi-task
architecture design systems that can automatically identify resource-effi
cient multi-task models with low inference costs and high task accuracy.
div>\n
\n
Biography
\n
\n
\nHui Guan is an Assistant Professor in the
College
of Information and Computer Sciences (CICS) at the University o
f Massachusetts Amherst\, the flagship campus of the UMass system. She rec
eived her Ph.D. in Electrical Engineering from
North Carolina State Univer
sity in 2020. Her research lies in the intersection between mac
hine learning and systems\, with an emphasis on improving the speed\, scal
ability\, and reliability of machine learning through innovations in algor
ithms and programming systems. Her current research focuses on both algori
thm and system optimizations of deep multi-task learning and graph machine
learning.
\n\n \n
DTSTART;TZID=America/New_York:20221111T120000
DTEND;TZID=America/New_York:20221111T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Hui Guan (University of Massachusetts Amherst) “Towards Accurate an
d Efficient Edge Computing Via Multi-Task Learning”
URL:https://www.clsp.jhu.edu/events/hui-guan-university-of-massachusetts-am
herst/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2022\,Guan\,November
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