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JMLR
2010
151views more  JMLR 2010»
15 years 1 months ago
Understanding the difficulty of training deep feedforward neural networks
Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with e...
Xavier Glorot, Yoshua Bengio
ICONIP
2008
15 years 8 months ago
Neural Network Regression for LHF Process Optimization
We present a system for regression using MLP neural networks with hyperbolic tangent functions in the input, hidden and output layer. The activation functions in the input and outp...
Miroslaw Kordos
ESANN
2001
15 years 8 months ago
Optimal transfer function neural networks
Neural networks use neurons of the same type in each layer but such architecture cannot lead to data models of optimal complexity and accuracy. Networks with architectures (number ...
Norbert Jankowski, Wlodzislaw Duch
NN
2007
Springer
15 years 6 months ago
Chaotic pattern transitions in pulse neural networks
In models of associative memory composed of pulse neurons, chaotic pattern transitions where the pattern retrieved by the network changes chaotically were found. The network is co...
Takashi Kanamaru
TNN
2010
139views Management» more  TNN 2010»
15 years 1 months ago
Identification of finite state automata with a class of recurrent neural networks
A class of recurrent neural networks is proposed and proven to be capable of identifying any discrete-time dynamical system. The application of the proposed network is addressed in...
Sung Hwan Won, Iickho Song, Sun-Young Lee, Cheol H...