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IJCAI
2007
14 years 11 months ago
Bayesian Inverse Reinforcement Learning
Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given the dynamics of the system and the behaviour of an e...
Deepak Ramachandran, Eyal Amir
JCC
2007
69views more  JCC 2007»
14 years 9 months ago
Discrimination of dynamical system models for biological and chemical processes
In technical chemistry, systems biology and biotechnology, the construction of predictive models has become an essential step in process design and product optimization. Accurate ...
Sönke Lorenz, Elmar Diederichs, Regina Telgma...
INFOCOM
2010
IEEE
14 years 8 months ago
Greedy Forwarding in Dynamic Scale-Free Networks Embedded in Hyperbolic Metric Spaces
In this paper we show that complex (scale-free) network topologies naturally emerge from hyperbolic metric spaces. The hyperbolic geometry can be used to facilitate maximally efï¬...
Fragkiskos Papadopoulos, Dmitri V. Krioukov, Mari&...
ECIR
2009
Springer
14 years 7 months ago
Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis
Abstract. In the community of sentiment analysis, supervised learning techniques have been shown to perform very well. When transferred to another domain, however, a supervised sen...
Songbo Tan, Xueqi Cheng, Yuefen Wang, Hongbo Xu
ISRR
2005
Springer
99views Robotics» more  ISRR 2005»
15 years 3 months ago
On the Probabilistic Foundations of Probabilistic Roadmap Planning
Why is probabilistic roadmap (PRM) planning probabilistic? How does the probability measure used for sampling a robot’s conï¬guration space affect the performance of a PRM plan...
David Hsu, Jean-Claude Latombe, Hanna Kurniawati