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138
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NN
1998
Springer
108views Neural Networks» more  NN 1998»
15 years 3 months ago
How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies
Learning long-term temporal dependencies with recurrent neural networks can be a difficult problem. It has recently been shown that a class of recurrent neural networks called NA...
Tsungnan Lin, Bill G. Horne, C. Lee Giles
IJCNN
2006
IEEE
15 years 9 months ago
Knowledge Representation and Possible Worlds for Neural Networks
— The semantics of neural networks can be analyzed mathematically as a distributed system of knowledge and as systems of possible worlds expressed in the knowledge. Learning in a...
Michael J. Healy, Thomas P. Caudell
130
Voted
IWANN
2005
Springer
15 years 9 months ago
Modeling Neural Processes in Lindenmayer Systems
Computing in nature as is the case with the human brain is an emerging research area in theoretical computer science. The present paper’s aim is to explore biological neural cell...
Carlos Martín-Vide, Tseren-Onolt Ishdorj
126
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NN
2000
Springer
167views Neural Networks» more  NN 2000»
15 years 3 months ago
Blind signal processing by the adaptive activation function neurons
The aim of this paper is to study an Information Theory based learning theory for neural units endowed with adaptive activation functions. The learning theory has the target to fo...
Simone Fiori