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Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, 2007
Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful technique for solving challenging reinforcement learning problems. Compared to traditional (e.g. value-function based) methods, neuroevolution is especially strong in domains where the state of the world is not fully known: The state can be disambiguated ...
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Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful technique for solving challenging reinforcement learning problems. Compared to traditional (e.g. value-function based) methods, neuroevolution is especially strong in domains where the state of the world is not fully known: The state can be disambiguated ...
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A neural network to design neural networks
IEEE Transactions on Circuits and Systems, 1991The design of the Hopfield associative memory is reformulated in terms of a constraint satisfaction problem. An electronic neural net capable of solving this problem in real time is proposed. Circuit solutions correspond to symmetrical zero-diagonal matrices that possess few spurious stable states.
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Neural Computation, 1999
A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN.
Søren Riis, Anders Krogh
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A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN.
Søren Riis, Anders Krogh
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Artificial Neural Networks [PDF]
, 2008 Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods.
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Introduction to Neural Networks: Biological Neural Network
2023Chapter 1 introduces the functional organization of the biological brain. The first section opens with the description of neurons, fundamental units of the brain. These are structures capable of collecting signals, processing them and delivering them to subsequent units.
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A Neural Network for Hyphenation
1992Abstract It is shown that although a neural net trained with backprop learning is able to achieve reasonable generalization performance on a hard linguistic pattern recognition problem (segmentation of spelling strings into syllables in Dutch), it remains inferior in performance to symbolic pattern matching algorithms, and even to a simple table ...
Daelemans, Walter, van den Bosch, Antal
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Image Style Transfer Using Convolutional Neural Networks
Computer Vision and Pattern Recognition, 2016Leon A. Gatys +2 more
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne T Penberthy +2 more
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