BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9//
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-FROM-URL:https://www.clsp.jhu.edu
X-WR-TIMEZONE:America/New_York
BEGIN:VTIMEZONE
TZID:America/New_York
X-LIC-LOCATION:America/New_York
BEGIN:STANDARD
DTSTART:20231105T020000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
RDATE:20241103T020000
TZNAME:EST
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20240310T020000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
RDATE:20250309T020000
TZNAME:EDT
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:ai1ec-20730@www.clsp.jhu.edu
DTSTAMP:20240328T083616Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:
Abstract
\nRaytheon BBN participated
in the IARPA MATERIAL program\, whose objective is to enable rapid develop
ment of language-independent methods for cross-lingual information retriev
al (CLIR). The challenging CLIR task of retrieving documents written (or s
poken) in one language so that they satisfy an information need expressed
in a different language is exacerbated by unique challenges posed by the M
ATERIAL program: limited training data for automatic speech recognition an
d machine translation\, scant lexical resources\, non-standardized orthogr
aphy\, etc. Furthermore\, the format of the queries and the “Query-Weighte
d Value” performance measure are non-standard and not previously studied i
n the IR community. In this talk\, we will describe the Raytheon BBN CLIR
system\, which was successful at addressing the above challenges and uniqu
e characteristics of the program.
\nBiography
\n
\n
Damianos Karakos has been at Raytheon BBN f
or the past nine years\, where he is currently a Senior Principal Engineer
\, Research. Before that\, he was research faculty at Johns Hopkins Univer
sity. He has worked on several Government projects (e.g.\, DARPA GALE\, DA
RPA RATS\, IARPA BABEL\, IARPA MATERIAL\, IARPA BETTER) and on a variety o
f HLT-related topics (e.g.\, speech recognition\, speech activity detectio
n\, keyword search\, information retrieval). He has published more than 60
peer-reviewed papers. His research interests lie at the intersection of h
uman language technology and machine learning\, with an emphasis on statis
tical methods. He obtained a PhD in Electrical Engineering from the Univer
sity of Maryland\, College Park\, in 2002.
\n
\n
DTSTART;TZID=America/New_York:20210924T120000
DTEND;TZID=America/New_York:20210924T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Damianos Karakos (Raytheon BBN) “The Raytheon BBN Cross-lingual Inf
ormation Retrieval System developed under the IARPA MATERIAL Program”
URL:https://www.clsp.jhu.edu/events/damianos-karakos/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2021\,Karakos\,September
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-22408@www.clsp.jhu.edu
DTSTAMP:20240328T083616Z
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
END:VEVENT
END:VCALENDAR