Authors
Matthew D Zeiler, M Ranzato, Rajat Monga, Min Mao, Kun Yang, Quoc Viet Le, Patrick Nguyen, Alan Senior, Vincent Vanhoucke, Jeffrey Dean, Geoffrey E Hinton
Publication date
2013/5/26
Conference
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Pages
3517-3521
Publisher
IEEE
Description
ABSTRACT Deep neural networks have recently become the gold standard for acoustic
modeling in speech recognition systems. The key computational unit of a deep network is a
linear projection followed by a point-wise non-linearity, which is typically a logistic function.
In this work, we show that we can improve generalization and make training of deep
networks faster and simpler by substituting the logistic units with rectified linear units. These
units are linear when their input is positive and zero otherwise. In a supervised setting, we ...
modeling in speech recognition systems. The key computational unit of a deep network is a
linear projection followed by a point-wise non-linearity, which is typically a logistic function.
In this work, we show that we can improve generalization and make training of deep
networks faster and simpler by substituting the logistic units with rectified linear units. These
units are linear when their input is positive and zero otherwise. In a supervised setting, we ...
Total citations
Scholar articles
MD Zeiler, M Ranzato, R Monga, M Mao, K Yang… - 2013 IEEE International Conference on Acoustics, …, 2013
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