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UID:ai1ec-20120@www.clsp.jhu.edu
DTSTAMP:20240328T155202Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:
Abstract
\nRobotics@Google’s mission
is to make robots useful in the real world through machine learning. We a
re excited about a new model for robotics\, designed for generalization ac
ross diverse environments and instructions. This model is focused on scala
ble data-driven learning\, which is task-agnostic\, leverages simulation\,
learns from past experience\, and can be quickly adapted to work in the r
eal-world through limited interactions. In this talk\, we’ll share some of
our recent work in this direction in both manipulation and locomotion app
lications.
\nBiography
\nCarolina Parada is a Senior Engineering Manager at Goo
gle Robotics. She leads the robot-mobility group\, which focuses on improv
ing robot motion planning\, navigation\, and locomotion\, using reinforcem
ent learning. Prior to that\, she led the camera perception team for self-
driving cars at Nvidia for 2 years. She was also a lead with Speech @ Goog
le for 7 years\, where she drove multiple research and engineering efforts
that enabled Ok Google\, the Google Assistant\, and Voice-Search. Carolina grew up in Venezuela and moved to the US
to pursue a B.S. and M.S. degree in Electrical Engineering at University
of Washington and her Phd at Johns Hopkins University at the Center for La
nguage and Speech Processing (CLSP).
DTSTART;TZID=America/New_York:20210423T120000
DTEND;TZID=America/New_York:20210423T131500
LOCATION:via Zoom
SEQUENCE:0
SUMMARY:Carolina Parada (Google AI) “State of Robotics @ Google”
URL:https://www.clsp.jhu.edu/events/carolina-parada-google-ai/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2021\,April\,Parada
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-21267@www.clsp.jhu.edu
DTSTAMP:20240328T155202Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract
\nIn this talk\, I present a
multipronged strategy for zero-shot cross-lingual Information Extraction\
, that is the construction of an IE model for some target language\, given
existing annotations exclusively in some other language. This work is par
t of the JHU team’s effort under the IARPA BETTER program. I explore data
augmentation techniques including data projection and self-training\, and
how different pretrained encoders impact them. We find through extensive e
xperiments and extension of techniques that a combination of approaches\,
both new and old\, leads to better performance than any one cross-lingual
strategy in particular.
\nBiography
\nMahsa
Yarmohammadi is an assistant research scientist in CLSP\, JHU\, who leads
state-of-the-art research in cross-lingual language and speech applicatio
ns and algorithms. A primary focus of Yarmohammadi’s research is using dee
p learning techniques to transfer existing resources into other languages
and to learn representations of language from multilingual data. She also
works in automatic speech recognition and speech translation. Yarmohammadi
received her PhD in computer science and engineering from Oregon Health &
Science University (2016). She joined CLSP as a post-doctoral fellow in 2
017.
\n
DTSTART;TZID=America/New_York:20220204T120000
DTEND;TZID=America/New_York:20220204T131500
LOCATION:Ames 234 Presented Virtually via Zoom https://wse.zoom.us/j/967351
83473
SEQUENCE:0
SUMMARY:Mahsa Yarmohammadi (Johns Hopkins University) “Data Augmentation fo
r Zero-shot Cross-Lingual Information Extraction”
URL:https://www.clsp.jhu.edu/events/mahsa-yarmohammadi-johns-hopkins-univer
sity-data-augmentation-for-zero-shot-cross-lingual-information-extraction/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2022\,February\,Yarmohammadi
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