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ICANN
2009
Springer
14 years 7 days ago
Scalable Neural Networks for Board Games
Learning to solve small instances of a problem should help in solving large instances. Unfortunately, most neural network architectures do not exhibit this form of scalability. Our...
Tom Schaul, Jürgen Schmidhuber
NN
2007
Springer
112views Neural Networks» more  NN 2007»
13 years 7 months ago
An augmented CRTRL for complex-valued recurrent neural networks
Real world processes with an “intensity” and “direction” component can be made complex by convenience of representation (vector fields, radar, sonar), and their processin...
Su Lee Goh, Danilo P. Mandic
ISADS
1999
IEEE
13 years 12 months ago
Emergence of Communication for Negotiation by a Recurrent Neural Network
We believe that communication in multi-agent system has two major meanings. One of them is to transmit one agent's observed information to the other. The other meaning is to ...
Katsunari Shibata, Koji Ito
ICES
2003
Springer
125views Hardware» more  ICES 2003»
14 years 25 days ago
Evolving Reinforcement Learning-Like Abilities for Robots
Abstract. In [8] Yamauchi and Beer explored the abilities of continuous time recurrent neural networks (CTRNNs) to display reinforcementlearning like abilities. The investigated ta...
Jesper Blynel
ICONIP
2004
13 years 9 months ago
Generalization in Learning Multiple Temporal Patterns Using RNNPB
Abstract. This paper examines the generalization capability in learning multiple temporal patterns by the recurrent neural network with parametric bias (RNNPB). Our simulation expe...
Masato Ito, Jun Tani