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COLT
2006
Springer
13 years 11 months ago
Learning Rational Stochastic Languages
Given a finite set of words w1, . . . , wn independently drawn according to a fixed unknown distribution law P called a stochastic language, an usual goal in Grammatical Inference ...
François Denis, Yann Esposito, Amaury Habra...
JMLR
2010
143views more  JMLR 2010»
13 years 2 months ago
Beware of the DAG!
Directed acyclic graph (DAG) models are popular tools for describing causal relationships and for guiding attempts to learn them from data. In particular, they appear to supply a ...
A. Philip Dawid
AAAI
2008
13 years 10 months ago
Maximum Entropy Inverse Reinforcement Learning
Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of recoveri...
Brian Ziebart, Andrew L. Maas, J. Andrew Bagnell, ...
IPMI
2009
Springer
14 years 8 months ago
Fully-Automated White Matter Hyperintensity Detection With Anatomical Prior Knowledge and Without FLAIR
This paper presents a method for detection of cerebral white matter hyperintensities (WMH) based on run-time PD-, T1-, and T2weighted structural magnetic resonance (MR) images of t...
Charles DeCarli, Christopher Schwarz, Evan Fletche...
AAAI
2011
12 years 7 months ago
Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation
This paper describes a new model for understanding natural language commands given to autonomous systems that perform navigation and mobile manipulation in semi-structured environ...
Stefanie Tellex, Thomas Kollar, Steven Dickerson, ...