In recent years, gradient-based LSTM recurrent neural networks (RNNs) solved many previously RNN-unlearnable tasks. Sometimes, however, gradient information is of little use for t...
In many practical engineering design problems, the form of objective function is not given explicitly in terms of design variables. Given the value of design variables, under this ...
This paper presents an study about a new Hybrid method GRASPES - for time series prediction, inspired in F. Takens theorem and based on a multi-start metaheuristic for combinatori...
Aranildo Rodrigues Lima Junior, Tiago Alessandro E...
This article initiates a rigorous theoretical analysis of the computational power of circuits that employ modules for computing winner-take-all. Computational models that involve ...
A neural network model of associative memory is presented which unifies the two historically more relevant enhancements to the basic Little-Hopfield discrete model: the graded resp...
Enrique Carlos Segura Meccia, Roberto P. J. Perazz...