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Computational learning theory Plenary Paper 171003
Uniformly sparse neural networks 171009
28 other sections not shown
AANET activation function analysis applied approximation architecture artificial neural networks automata backpropagation behavior binary classification coefficients Computer configuration convergence correlation defined derived dynamics Dystal elements equations error example faults feature feedforward feedforward networks Figure global grammar Henon map hidden layer hidden nodes hidden units IEEE input matrix input vectors iterations learning algorithm learning potential function linear Lyapunov Lyapunov exponents mapping mathematical matrix method minimization multilayer multilayer perceptron neurons noise nonlinear null space number of hidden objective function optimal optimisation oscillators output layer output node parameters pattern recognition performance perturbation phase transition pixels principal component probability distribution problem propagation properties random receptive field receptors representation represents rule samples self-organizing sigmoid sigmoid functions signals simulated simulated annealing solution space statistical structure supervised learning target technique theory threshold tooth training patterns training set transformation two-layer perceptron variables vector visual weights