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This paper reports a novel deep architecture referred to as Maxout network In Network (MIN), which can enhance model discriminability and facilitate the process of information abstraction within the receptive field.
Effects of noise letters upon the identification of a target letter in a nonsearch task
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On the number of linear regions of deep neural networks
G. F. Montufar, R. Pascanu, K. Cho, and Y. Bengio · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Matconvnet-convolutional neural networks for matlab
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From maxout to channel-out: Encoding information on sparse pathways
Q. Wang and J. JaJa · 2014
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Maxout networks
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Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
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Regularization of neural networks using dropconnect
L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus · 2013
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Stochastic pooling for regularization of deep convolutional neural networks
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M. D. Zeiler and R. Fergus · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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