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Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks.
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Vehicle Recognition Using Rule Based Methods
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A taxonomy of global optimization methods based on response surfaces
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Learning multiple layers of features from tiny images
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A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning
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Gaussian process optimization in the bandit setting: No regret and experimental design
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Algorithms for hyper-parameter optimization
J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl · 2011
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Sequential model-based optimization for general algorithm configuration
F. Hutter, H. Hoos, and K. Leyton-Brown · 2011
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Reading digits in natural images with unsupervised feature learning
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Neural Networks: Tricks of the Trade - Second Edition
G. Montavon, G. Orr, and K.-R. Müller, editors · 2012
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Random search for hyper-parameter optimization
J. Bergstra and Y. Bengio · 2012
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Practical Bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
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Stochastic gradient tricks
L. Bottou · 2012
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OpenML: Networked science in machine learning
J. Vanschoren, J. van Rijn, B. Bischl, and L. Torgo · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Scalable Bayesian optimization using Deep Neural Networks
J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M. M. A. Patwary, Prabhat, and R. P. Adams · 2015
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Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
T. Domhan, J. T. Springenberg, and F. Hutter · 2015
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Fast cross-validation via sequential testing
T. Krueger, D. Panknin, and M. Braun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Entropy search for information-efficient global optimization
P. Hennig and C. Schuler · 2012
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Collaborative hyperparameter tuning
R. Bardenet, M. Brendel, B. Kégl, and M. Sebag · 2013
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Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
J. Bergstra, D. Yamins, and D. Cox · 2013
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Multi-task Bayesian optimization
K. Swersky, J. Snoek, and R. Adams · 2013
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emcee : The MCMC Hammer
D. Foreman-Mackey, D. W. Hogg, D. Lang, and J. Goodman · 2013
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Freeze-thaw Bayesian optimization
K. Swersky, J. Snoek, and R. Adams · 2014
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Probabilistic backpropagation for scalable learning of Bayesian neural networks
J. Hernández-Lobato and R. Adams · 2015
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Weight uncertainty in neural networks
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
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Efficient hyperparameter optimization and infinitely many armed bandits
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2016
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Taking the human out of the loop: A Review of Bayesian Optimization
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Bayesian optimization with robust Bayesian neural networks
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Hyperband: Bandit-based configuration evaluation for hyperparameter optimization
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Learning curve prediction with Bayesian neural networks
A. Klein, S. Falkner, J. T. Springenberg, and F. Hutter · 2017
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