Results 281 to 290 of about 354,413 (309)
Some of the next articles are maybe not open access.
2018
In Chapter 9, we looked at how convolutional neural networks (CNNs) improve upon the traditional neural network architecture for image classification. Although CNNs perform very well for image classification in which image translation and rotation are taken care of, they do not necessarily help in identifying temporal patterns.
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In Chapter 9, we looked at how convolutional neural networks (CNNs) improve upon the traditional neural network architecture for image classification. Although CNNs perform very well for image classification in which image translation and rotation are taken care of, they do not necessarily help in identifying temporal patterns.
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2019
In the previous chapter, CNNs provided a way for neural networks to learn a hierarchy of weights, resembling that of n-gram classification on the text. This approach proved to be very effective for sentiment analysis, or more broadly text classification.
Uday Kamath, John Liu, James Whitaker
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In the previous chapter, CNNs provided a way for neural networks to learn a hierarchy of weights, resembling that of n-gram classification on the text. This approach proved to be very effective for sentiment analysis, or more broadly text classification.
Uday Kamath, John Liu, James Whitaker
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1999
From the Publisher: With applications ranging from motion detection to financial forecasting, recurrent neural networks (RNNs) have emerged as an interesting and important part of neural network research. Recurrent Neural Networks: Design and Applications reflects the tremendous, worldwide interest in and virtually unlimited potential of RNNs ...
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From the Publisher: With applications ranging from motion detection to financial forecasting, recurrent neural networks (RNNs) have emerged as an interesting and important part of neural network research. Recurrent Neural Networks: Design and Applications reflects the tremendous, worldwide interest in and virtually unlimited potential of RNNs ...
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1995
Neural networks have attracted much attention lately as a powerful tool of automatic learning. Of particular interest is the class of recurrent networks which allow for loops and cycles and thus give rise to dynamical systems, to flexible behavior, and to computation. This paper reviews the recent findings that mathematically quantify the computational
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Neural networks have attracted much attention lately as a powerful tool of automatic learning. Of particular interest is the class of recurrent networks which allow for loops and cycles and thus give rise to dynamical systems, to flexible behavior, and to computation. This paper reviews the recent findings that mathematically quantify the computational
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IEEE Transactions on Neural Networks and Learning Systems, 2020
Yundi Chu, Juntao Fei, Shixi Hou
exaly
Yundi Chu, Juntao Fei, Shixi Hou
exaly