The dropout learning algorithm

Dropout is a recently introduced algorithm for training neural networks by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommoda...

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Veröffentlicht in:	Artificial intelligence Jg. 210; S. 78 - 122
Hauptverfasser:	Baldi, Pierre, Sadowski, Peter
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier B.V 01.05.2014 Elsevier
Schlagworte:	Applied sciences Approximation Artificial intelligence Backpropagation Computer science; control theory; systems Connectionism. Neural networks Detection, estimation, filtering, equalization, prediction Dropouts Ensemble Exact sciences and technology Functions (mathematics) Geometric mean Information, signal and communications theory Learning and adaptive systems Logistics Machine learning Mathematical analysis Networks Neural networks Regularization Signal and communications theory Signal, noise Sparse representations Stochastic gradient descent Stochastic neurons Stochasticity Telecommunications and information theory Training Variance minimization Backpropagation Neural networks Geometric mean Variance minimization Machine learning Ensemble Sparse representations Stochastic gradient descent Regularization Stochastic neurons Ergodic theory On line Self consistency Dynamic properties Modeling Adaptive method Varying speed Variance Gradient descent Backpropagation algorithm Sparse representation Transfer function Learning algorithm Logistic function Static properties Minimization Neural network Independent variable Linearity Artificial intelligence Logistics sparse representations backpropagation neural networks regularization variance minimization stochastic neurons ensemble machine learning stochastic gradient descent geometric mean
ISSN:	0004-3702, 1872-7921
Online-Zugang:	Volltext
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