Multilayer feedforward networks are universal approximators
Neural Networks
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
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ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
A fast learning algorithm for deep belief nets
Neural Computation
Learning Deep Architectures for AI
Foundations and Trends® in Machine Learning
Hybrid artificial neural networks: models, algorithms and data
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
Genetic evolution of the topology and weight distribution of neural networks
IEEE Transactions on Neural Networks
Cybernetics of Vision Systems: Toward an Understanding of Putative Functions of the Outer Retina
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The current paper introduces the concept of neural diversity machines (NDM) which, refers to hybrid artificial neural networks (HANN) with conditions on the minimum number of functions available to the network, amongst several other properties. The paper demonstrates how NDM networks can be optimized for solving different problems. The results demonstrate the feasibility of the approach and bolster some of the biological and computational arguments in favor of neural diversity. A substantial number of optimization experiments were conducted, generating a corresponding number of diverse neural architectures, which revealed several unexpected statistics, including the relative commonality of nodes combining inner-product and Gaussian functions. The paper confirms the advantages of HANN, demonstrates the potential of increasing the focus on neural diversity and hints at possible new neural computational strategies.