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May 2010
Ph.D. Thesis at UC Berkeley, May, 2010
Leon Barrett
I present a computational architecture designed to capture certain properties essential
to actions, including compositionality, concurrency, quick reactions, and resilience in the face
of unexpected events. It uses a structured internal state model and complex inference about
the environment to inform decision-making. The properties above are achieved by combining
interacting procedural and probabilistic representations, so that the structure of actions is captured
by Petri Nets, which are informed by, and affect, a model of the world represented as a Probabilistic
Relational Model. I give both a theoretical analysis of the architecture and a demonstration of its
use in a simulated robotic environment.
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September 2008
Neural Computation, Vol. 20, No. 9: 2361-2378
Leon Barrett, Jerome Feldman, and Liam Mac Dermed
We propose a mechanism for binding and automatic inference in humans that does not rely on temporal synchrony. An
online example and demo is available.
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July 2008
ICML 2008
Leon Barrett and Srini Narayanan
We give an algorithm for learning all optimal policies when performing reinforcement learning with multiple rewards.
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May 2006
ICSI Tech Report TR-06-001
Leon Barrett, Jerome Feldman, and Liam Mac Dermed
We propose a mechanism for binding and automatic inference in humans that does not rely on temporal synchrony. An
online example and demo is available.
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2006
in ACL
Slav Petrov, Leon Barrett, Romain Thibaux, Dan Klein
Automatically learning more informative grammars gives grammars that capture linguistic features and also gives high parsing accuracy; we got an F1 score of 90.2.
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2006
in CONLL
Slav Petrov, Leon Barrett, Dan Klein
We examine parsing text by using a hierarchical mixture of grammars. This helps capture non-local correlations, but unfortunately these make up a small amount of all correlations to be captured.
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November 2005
in Graphite
Leon Barrett, Patrick Coleman, Nisha Sudarsanam, Karan Singh, and Cindy Grimm
My first published paper! Automatic camera placement and multiple-camera interpolation, with Cindy Grimm and others.