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UID:ai1ec-21072@www.clsp.jhu.edu
DTSTAMP:20240329T113701Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:
Abstract
\nEmotion has intrigued re
searchers for generations. This fascination has permeated the engineering
community\, motivating the development of affective computing methods. How
ever\, human emotion remains notoriously difficult to accurately detect. A
s a result\, emotion classification techniques are not always effective wh
en deployed. This is a problem because we are missing out on the potentia
l that emotion recognition provides: the opportunity to automatically meas
ure an aspect of behavior that provides critical insight into our health a
nd wellbeing\, insight that is not always easily accessible. In this talk
\, I will discuss our efforts in developing emotion recognition approaches
that are effective in natural environments and demonstrate how these appr
oaches can be used to support mental health.
\n\nBiography
\n\nEmily Mower Provost is an
Associate Professor in Computer Science and Engineering and Toyota Faculty
Scholar at the University of Michigan. She received her Ph.D. in Electric
al Engineering from the University of Southern California (USC)\, Los Ange
les\, CA in 2010. She has been awarded a National Science Foundation CAREE
R Award (2017)\, the Oscar Stern Award for Depression Research (2015)\, a
National Science Foundation Graduate Research Fellowship (2004-2007). She
is a co-author on the paper\, “Say Cheese vs. Smile: Reducing Speech-Relat
ed Variability for Facial Emotion Recognition\,” winner of Best Student Pa
per at ACM Multimedia\, 2014\, and a co-author of the winner of the Classi
fier Sub-Challenge event at the Interspeech 2009 emotion challenge. Her re
search interests are in human-centered speech and video processing\, multi
modal interfaces design\, and speech-based assistive technology. The goals
of her research are motivated by the complexities of the perception and e
xpression of human behavior.
DTSTART;TZID=America/New_York:20211206T120000
DTEND;TZID=America/New_York:20211206T131500
LOCATION:Maryland Hall 110 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Emily Mower-Provost (University of Michigan) “Automatically Measuri
ng Emotion from Speech: New Methods to Move from the Lab to the Real World
”
URL:https://www.clsp.jhu.edu/events/emily-mower-provost-university-of-michi
gan/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2021\,December\,Mower-Provost
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UID:ai1ec-22395@www.clsp.jhu.edu
DTSTAMP:20240329T113701Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract
\nRecursive call
s over recursive data are widely useful for generating probability distrib
utions\, and probabilistic programming allows computations over these dist
ributions to be expressed in a modular and intuitive way. Exact inference
is also useful\, but unfortunately\, existing probabilistic programming la
nguages do not perform exact inference on recursive calls over recursive d
ata\, forcing programmers to code many applications manually. We introduce
a probabilistic language in which a wide variety of recursion can be expr
essed naturally\, and inference carried out exactly. For instance\, probab
ilistic pushdown automata and their generalizations are easy to express\,
and polynomial-time parsing algorithms for them are derived automatically.
We eliminate recursive data types using program transformations related t
o defunctionalization and refunctionalization. These transformations are a
ssured correct by a linear type system\, and a successful choice of transf
ormations\, if there is one\, is guaranteed to be found by a greedy algori
thm. I will also describe the implementation of this language in two phase
s: first\, compilation to a factor graph grammar\, and second\, computing
the sum-product of the factor graph grammar.
\n
\nBiography
\nDavid Chiang (PhD\, University of Pennsylvania\, 2004) is an assoc
iate professor in the Department of Computer Science and Engineering at th
e University of Notre Dame. His research is on computational models for le
arning human languages\, particularly how to translate from one language t
o another. His work on applying formal grammars and machine learning to tr
anslation has been recognized with two best paper awards (at ACL 2005 and
NAACL HLT 2009). He has received research grants from DARPA\, NSF\, Google
\, and Amazon\, has served on the executive board of NAACL and the editori
al board of Computational Linguistics and JAIR\, and is currently on the e
ditorial board of Transactions of the ACL.
DTSTART;TZID=America/New_York:20221017T120000
DTEND;TZID=America/New_York:20221017T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:David Chiang (University of Notre Dame) “Exact Recursive Probabilis
tic Programming with Colin McDonald\, Darcey Riley\, Kenneth Sible (Notre
Dame) and Chung-chieh Shan (Indiana)”
URL:https://www.clsp.jhu.edu/events/david-chiang-university-of-notre-dame/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2022\,Chiang\,October
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