Expanding variational autoencoders for learning and exploiting latent representations in search distributions-Reference-Cited by-同舟云学术

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Expanding variational autoencoders for learning and exploiting latent representations in search distributions

Published:2018-07-02 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Garciarena Unai¹,Santana Roberto¹,Mendiburu Alexander¹

Affiliation:

1. University of the Basque Country, Donostia-San Sebastian, Spain

Funder

University of the Basque Country

Basque Government

Spanish Ministry of Economy, Industry and Competitiveness

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3205455.3205645

Reference34 articles.

1. A. E. I. Brownlee J. A. McCall and S. K. Shakya. 2012. The Markov Network Fitness Model. In Markov Networks in Evolutionary Computation S. Shakya and R. Santana (Eds.). Springer 125--140. A. E. I. Brownlee J. A. McCall and S. K. Shakya. 2012. The Markov Network Fitness Model. In Markov Networks in Evolutionary Computation S. Shakya and R. Santana (Eds.). Springer 125--140.

2. D. Cho and B. Zhang. 2004. Evolutionary Continuous Optimization by Distribution Estimation with Variational Bayesian Independent Component Analyzers Mixture Model. In Parallel Problem Solving from Nature (PPSN VIII) Vol. 3242. Springer 212--221. D. Cho and B. Zhang. 2004. Evolutionary Continuous Optimization by Distribution Estimation with Variational Bayesian Independent Component Analyzers Mixture Model. In Parallel Problem Solving from Nature (PPSN VIII) Vol. 3242. Springer 212--221.

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