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» Quantitative Models and Implicit Complexity
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ICML
2008
IEEE
14 years 8 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
COMPLEXITY
2004
165views more  COMPLEXITY 2004»
13 years 7 months ago
Sex promotes gamete selection: A quantitative comparative study of features favoring the evolution of sex
: Explaining the maintenance of sexual reproduction remains one of the greatest challenges in biology. The theoretical oddity of sex is based on at least three advantages that asex...
Klaus Jaffe
EEF
2000
13 years 11 months ago
Distributed and Structured Analysis Approaches to Study Large and Complex Systems
Both the logic and the stochastic analysis of discrete-state systems are hindered by the combinatorial growth of the state space underlying a high-level model. In this work, we con...
Gianfranco Ciardo
GECCO
2009
Springer
148views Optimization» more  GECCO 2009»
13 years 5 months ago
Genetic programming for quantitative stock selection
We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors identified by...
Ying L. Becker, Una-May O'Reilly
TIP
2011
164views more  TIP 2011»
13 years 2 months ago
Multiregion Image Segmentation by Parametric Kernel Graph Cuts
Abstract—The purpose of this study is to investigate multiregion graph cut image partitioning via kernel mapping of the image data. The image data is transformed implicitly by a ...
Mohamed Ben Salah, Amar Mitiche, Ismail Ben Ayed