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Robotics@Google’s mission is to make robots useful i n the real world through machine learning. We are excited about a new mode l for robotics\, designed for generalization across diverse environments a nd instructions. This model is focused on scalable data-driven learning\, which is task-agnostic\, leverages simulation\, learns from past experienc e\, and can be quickly adapted to work in the real-world through limited i nteractions. In this talk\, we’ll share some of our recent work in this di rection in both manipulation and locomotion applications.

\n< strong>Biography

\nCarolina Parad a is a Senior Engineering Manager at Google Robotics. She leads the robot-mobility group\, which focuses on improving robot motion planning\, navigation\, and locomotion\, using reinforcement 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 @ Google for 7 years\, where she drove multiple research and engineering efforts that enabled Ok Google\, t he Google Assistant\, and Voice-Search. Carolina< /span> 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 a t Johns Hopkins University at the Center for Language and Speech Processin g (CLSP).

\n X-TAGS;LANGUAGE=en-US:2021\,April\,Parada END:VEVENT BEGIN:VEVENT UID:ai1ec-22395@www.clsp.jhu.edu DTSTAMP:20230324T002927Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nRecursive calls over recursive data are widely useful for generating probability distributions\, and probabilistic programming allows computations over these distributions to be expressed in a modular and intuitive way. Exact inference is also useful\, but unfortunately\, ex isting probabilistic programming languages do not perform exact inference on recursive calls over recursive data\, forcing programmers to code many applications manually. We introduce a probabilistic language in which a wi de variety of recursion can be expressed naturally\, and inference carried out exactly. For instance\, probabilistic pushdown automata and their gen eralizations are easy to express\, and polynomial-time parsing algorithms for them are derived automatically. We eliminate recursive data types usin g program transformations related to defunctionalization and refunctionali zation. These transformations are assured correct by a linear type system\ , and a successful choice of transformations\, if there is one\, is guaran teed to be found by a greedy algorithm. I will also describe the implement ation of this language in two phases: first\, compilation to a factor grap h grammar\, and second\, computing the sum-product of the factor graph gra mmar.\n\nBiography\nDavid Chiang (PhD\, University of Pennsylvania\, 2004) is an associate professor in the Department of Computer Science and Engin eering at the University of Notre Dame. His research is on computational m odels for learning human languages\, particularly how to translate from on e language to another. His work on applying formal grammars and machine le arning to translation has been recognized with two best paper awards (at A CL 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 editorial board of Computational Linguistics and JAIR\, and is curren tly on the editorial 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-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n**Abstr
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Recursive calls over recursive data are w
idely useful for generating probability distributions\, and probabilistic
programming allows computations over these distributions to be expressed i
n a modular and intuitive way. Exact inference is also useful\, but unfort
unately\, existing probabilistic programming languages do not perform exac
t inference on recursive calls over recursive data\, forcing programmers t
o code many applications manually. We introduce a probabilistic language i
n which a wide variety of recursion can be expressed naturally\, and infer
ence carried out exactly. For instance\, probabilistic pushdown automata a
nd their generalizations are easy to express\, and polynomial-time parsing
algorithms for them are derived automatically. We eliminate recursive dat
a types using program transformations related to defunctionalization and r
efunctionalization. These transformations are assured correct by a linear
type system\, and a successful choice of transformations\, if there is one
\, is guaranteed to be found by a greedy algorithm. I will also describe t
he implementation of this language in two phases: first\, compilation to a
factor graph grammar\, and second\, computing the sum-product of the fact
or graph grammar.

\n\nDavid Chiang (PhD\,
University of Pennsylvania\, 2004) is an associate professor in the Depart
ment of Computer Science and Engineering at the University of Notre Dame.
His research is on computational models for learning human languages\, par
ticularly how to translate from one language to another. His work on apply
ing formal grammars and machine learning to translation has been recognize
d with two best paper awards (at ACL 2005 and NAACL HLT 2009). He has rece
ived research grants from DARPA\, NSF\, Google\, and Amazon\, has served o
n the executive board of NAACL and the editorial board of Computational Li
nguistics and JAIR\, and is currently on the editorial board of Transactio
ns of the ACL.

\n
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