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» Structure learning of Bayesian networks using constraints
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ICANN
2010
Springer
15 years 5 months ago
Exploring Continuous Action Spaces with Diffusion Trees for Reinforcement Learning
We propose a new approach for reinforcement learning in problems with continuous actions. Actions are sampled by means of a diffusion tree, which generates samples in the continuou...
Christian Vollmer, Erik Schaffernicht, Horst-Micha...
IJCNN
2007
IEEE
15 years 11 months ago
Risk Assessment Algorithms Based on Recursive Neural Networks
— The assessment of highly-risky situations at road intersections have been recently revealed as an important research topic within the context of the automotive industry. In thi...
Alejandro Chinea Manrique De Lara, Michel Parent
CORR
2008
Springer
116views Education» more  CORR 2008»
15 years 4 months ago
To which extend is the "neural code" a metric ?
Here is proposed a review of the different choices to structure spike trains, using deterministic metrics. Temporal constraints observed in biological or computational spike train...
Bruno Cessac, Horacio Rostro-González, Juan...
COMPLEXITY
2010
124views more  COMPLEXITY 2010»
15 years 2 months ago
Spatially embedded dynamics and complexity
To gain a deeper understanding of the impact of spatial embedding on the dynamics of complex systems we employ a measure of interaction complexity developed within neuroscience us...
Christopher L. Buckley, Seth Bullock, Lionel Barne...
IWCM
2004
Springer
15 years 10 months ago
Tracking Complex Objects Using Graphical Object Models
We present a probabilistic framework for component-based automatic detection and tracking of objects in video. We represent objects as spatio-temporal two-layer graphical models, w...
Leonid Sigal, Ying Zhu, Dorin Comaniciu, Michael J...