2103 search results - page 306 / 421 » Approximate Learning of Dynamic Models |
148
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NIPS
2007
15 years 6 months ago
2007
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
118
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NIPS
1997
15 years 6 months ago
1997
In many real world tasks, only a small fraction of the available inputs are important at any particular time. This paper presents a method for ascertaining the relevance of inputs...
186
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FCSC
2010
15 years 2 months ago
2010
Graphical models have become the basic framework for topic based probabilistic modeling. Especially models with latent variables have proved to be effective in capturing hidden str...
153
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IFIP3
1998
15 years 6 months ago
1998
Development of a wide spread project intended to teaching Computer Science, integrating a considerable number of students all over a country with big geographical extension and sc...
159
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NECO
2008
15 years 4 months ago
2008
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...