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IJCAI
2007
13 years 10 months ago
Compiling Bayesian Networks by Symbolic Probability Calculation Based on Zero-Suppressed BDDs
Compiling Bayesian networks (BNs) is one of the hot topics in the area of probabilistic modeling and processing. In this paper, we propose a new method of compiling BNs into multi...
Shin-ichi Minato, Ken Satoh, Taisuke Sato
NIPS
2004
13 years 10 months ago
Dynamic Bayesian Networks for Brain-Computer Interfaces
We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN...
Pradeep Shenoy, Rajesh P. N. Rao
NECO
2008
170views more  NECO 2008»
13 years 8 months ago
Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
Deep Belief Networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton et al., along with a greedy layer-wis...
Nicolas Le Roux, Yoshua Bengio
CC
1998
Springer
100views System Software» more  CC 1998»
13 years 8 months ago
Symmetric Alternation Captures BPP
We introduce the natural class SP 2 containing those languages which may be expressed in terms of two symmetric quanti ers. This class lies between P 2 and P 2 P 2 and naturall...
Alexander Russell, Ravi Sundaram
ACTA
2008
88views more  ACTA 2008»
13 years 7 months ago
Weighted height of random trees
We consider a model of random trees similar to the split trees of Devroye [30] in which a set of items is recursively partitioned. Our model allows for more flexibility in the cho...
Nicolas Broutin, Luc Devroye, Erin McLeish