— This paper applies a recently developed neural network called plausible neural network (PNN) to function approximation. Instead of using error correction, PNN estimates the mut...
Modeling and formally analyzing active network systems and protocols is quite challenging, due to their highly dynamic nature and the need for new network models. We propose a wid...
This paper studies the use of discrete-time recurrent neural networks for predicting the next symbol in a sequence. The focus is on online prediction, a task much harder than the c...
In measuring the overall security of a network, a crucial issue is to correctly compose the measure of individual components. Incorrect compositions may lead to misleading results...
This poster shows an artificial neural network capable of learning a temporal sequence. Directly inspired from a hippocampus model [Banquet et al, 1998], this architecture allows ...