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Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.
A new method for locating the maximum point of an arbitrary multipeak curve in the presence of noise
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The application of Bayesian methods for seeking the extremum
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Bayesian back-propagation
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A practical Bayesian framework for backpropagation networks
MacKay, D. J · 1992
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Keeping neural networks simple by minimizing the description length of the weights
Hinton, G. E. and van Camp, D · 1993
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Bayesian learning for neural networks
Neal, R. M · 1995
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Computing with infinite networks
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Slice sampling
Neal, R · 2000
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A taxonomy of global optimization methods based on response surfaces
Jones, D. R · 2001
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Bayesian methods for neural networks
De Freitas, J. F · 2003
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Sparse Gaussian processes using pseudo-inputs
Snelson, E. and Ghahramani, Z · 2005
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Pattern Recognition and Machine Learning
Bishop, C. M · 2006
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Using deep belief nets to learn covariance kernels for Gaussian processes
Hinton, G. E. and Salakhutdinov, R · 2008
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Practical Bayesian Optimization
Lizotte, D · 2008
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Handling sparsity via the horseshoe
Carvalho, C. M., Polson, N. G., and Scott, J. G · 2009
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Gaussian processes for global optimization
Osborne, M. A., Garnett, R., and Roberts, S. J · 2009
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Variational learning of inducing variables in sparse Gaussian processes
Titsias, M. K · 2009
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A Bayesian interactive optimization approach to procedural animation design
Brochu, E., Brochu, T., and de Freitas, N · 2010
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Bayesian optimization for sensor set selection
Garnett, R., Osborne, M. A., and Roberts, S. J · 2010
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Dealing with asynchronicity in parallel Gaussian process based global optimization
Ginsbourger, D. and Riche, R. L · 2010
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Optimization under unknown constraints, 2010
Gramacy, R. B. and Lee, H. K. H · 2010
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Marginalized neural network mixtures for large-scale regression
Lázaro-Gredilla, M. and Figueiras-Vidal, A. R · 2010
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Gaussian process optimization in the bandit setting: no regret and experimental design
Srinivas, N., Krause, A., Kakade, S., and Seeger, M · 2010
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Algorithms for hyper-parameter optimization
Bergstra, J. S., Bardenet, R., Bengio, Y., and Kégl, B · 2011
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Convergence rates of efficient global optimization algorithms
Lin, M., Chen, Q., and Yan, S · 2013
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Bayesian Optimization and Semiparametric Models with Applications to Assistive Technology
Snoek, J · 2013
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Multi-task Bayesian optimization
Swersky, K., Snoek, J., and Adams, R. P · 2013
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Regularization of neural networks using dropconnect
Wan, L., Zeiler, M. D., Zhang, S., LeCun, Y., and Fergus, R · 2013
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Bayesian optimization in high dimensions via random embeddings
Wang, Z., Zoghi, M., Hutter, F., Matheson, D., and de Freitas, N · 2013
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Bayesian optimization with unknown constraints
Gelbart, M. A., Snoek, J., and Adams, R. P · 2014
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Bull, A. D · 2011
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Portfolio allocation for Bayesian optimization
Hoffman, M., Brochu, E., and de Freitas, N · 2011
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Sequential model-based optimization for general algorithm configuration
Hutter, F., Hoos, H. H., and Leyton-Brown, K · 2011
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Contextual Gaussian process bandit optimization
Krause, A. and Ong, C. S · 2011
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Random search for hyper-parameter optimization
Bergstra, J. and Bengio, Y · 2012
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Exponential regret bounds for Gaussian process bandits with deterministic observations
de Freitas, N., Smola, A. J., and Zoghi, M · 2012
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Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2012
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Multimodal neural language models
Kiros, R., Salakhutdinov, R., and Zemel, R. S · 2014
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Deeply supervised nets
Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., and Tu, Z · 2014
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Microsoft COCO: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
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Neural variational inference and learning in belief networks
Mnih, A. and Gregor, K · 2014
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Automated machine learning using stochastic algorithm tuning
Nickson, T., Osborne, M. A., Reece, S., and Roberts, S · 2014
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Stochastic back-propagation and variational inference in deep latent Gaussian models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Input warping for Bayesian optimization of non-stationary functions
Snoek, J., Swersky, K., Zemel, R. S., and Adams, R. P · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. A · 2014
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Initializing Bayesian hyperparameter optimization via meta-learning
Feurer, M., Springenberg, T., and Hutter, F · 2015
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Show, attend and tell: Neural image caption generation with visual attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., and Bengio, Y · 2015
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Recurrent neural network regularization
Zaremba, W., Sutskever, I., and Vinyals, O · 2015
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