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UID:ai1ec-20120@www.clsp.jhu.edu
DTSTAMP:20240328T191127Z
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-21487@www.clsp.jhu.edu
DTSTAMP:20240328T191127Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract
\nEnormous amounts of ever-changing knowledge are a
vailable online in diverse textual styles and diverse formats. Recent adva
nces in deep learning algorithms and large-scale datasets are spurring pro
gress in many Natural Language Processing (NLP) tasks\, including question
answering. Nevertheless\, these models cannot scale up when task-annotate
d training data are scarce. This talk presents my lab’s work toward buildi
ng general-purpose models in NLP and how to systematically evaluate them.
First\, I present a general model for two known tasks of question answerin
g in English and multiple languages that are robust to small domain shifts
. Then\, I show a meta-training approach that can solve a variety of NLP
tasks with only using a few examples and introduce a benchmark to evaluate
cross-task generalization. Finally\, I discuss neuro-symbolic appr
oaches to address more complex tasks by eliciting knowledge from structure
d data and language models.
\n\nBiography
\n\nHanna Hajishirzi is an Assistant Professor in the Paul G. Allen Schoo
l of Computer Science & Engineering at the University of Washington and a
Senior Research Manager at the Allen Institute for AI. Her research spans
different areas in NLP and AI\, focusing on developing general-purpose mac
hine learning algorithms that can solve many NLP tasks. Applications for t
hese algorithms include question answering\, representation learning\, gre
en AI\, knowledge extraction\, and conversational dialogue. Honors include
the NSF CAREER Award\, Sloan Fellowship\, Allen Distinguished Investigato
r Award\, Intel rising star award\, best paper and honorable mention award
s\, and several industry research faculty awards. Hanna received her PhD f
rom University of Illinois and spent a year as a postdoc at Disney Researc
h and CMU.
DTSTART;TZID=America/New_York:20220225T120000
DTEND;TZID=America/New_York:20220225T131500
LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j
/96735183473
SEQUENCE:0
SUMMARY:Hanna Hajishirzi (University of Washington & Allen Institute for AI
) “Toward Robust\, Knowledge-Rich NLP”
URL:https://www.clsp.jhu.edu/events/hanna-hajishirzi-university-of-washingt
on-allen-institute-for-ai-toward-robust-knowledge-rich-nlp/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2022\,February\,Hajishirzi
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-23308@www.clsp.jhu.edu
DTSTAMP:20240328T191127Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract
\nBiases in datasets\, or un
intentionally introduced spurious cues\, are a common source of misspecifi
cation in machine learning. Performant models trained on such data can gen
der stereotype or be brittle under distribution shift. In this talk\, we
present several results in multimodal and question answering applications
studying sources of dataset bias\, and several mitigation methods. We pro
pose approaches where known dimensions of dataset bias are explicitly fact
ored out of a model during learning\, without needing to modify data. Fina
lly\, we ask whether dataset biases can be attributable to annotator behav
ior during annotation. Drawing inspiration from work in psychology on cogn
itive biases\, we show certain behavioral patterns are highly indicative o
f the creation of problematic (but valid) data instances in question answe
ring. We give evidence that many existing observations around how dataset
bias propagates to models can be attributed to data samples created by ann
otators we identify.
\nBiography
\nMark Ya
tskar is an Assistant Professor at University of Pennsylvania in th
e department of Computer and Information Science. He did his PhD at Univer
sity of Washington co-advised by Luke Zettlemoyer and Ali Farhadi. He was
a Young Investigator at the Allen Institute for Artificial Intelligence fo
r several years working with their computer vision team\, Prior. His work
spans Natural Language Processing\, Computer Vision\, and Fairness in Mach
ine Learning. He received a Best Paper Award at EMNLP for work on gender b
ias amplification\, and his work has been featured in Wired and the New Yo
rk Times.
\n
DTSTART;TZID=America/New_York:20230210T120000
DTEND;TZID=America/New_York:20230210T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Mark Yatskar (University of Pennsylvania) “Understanding Dataset Bi
ases: Behavioral Indicators During Annotation and Contrastive Mitigations”
URL:https://www.clsp.jhu.edu/events/mark-yatskar-university-of-pennsylvania
/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2023\,February\,Yatskar
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