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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
European Conference on Computer Vision, 2016We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32\(\times \) memory saving.
Mohammad Rastegari +3 more
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Dropout: a simple way to prevent neural networks from overfitting
Journal of machine learning research, 2014Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks.
Nitish Srivastava +4 more
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Domain-Adversarial Training of Neural Networks
Journal of machine learning research, 2015We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions.
Yaroslav Ganin +7 more
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2009
Neural networks are a class of intelligent learning machines establishing the relationships between descriptors of real-world objects. As optimisation tools they are also a class of computational algorithms implemented using statistical/numerical techniques for parameter estimate, model selection, and generalisation enhancement.
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Neural networks are a class of intelligent learning machines establishing the relationships between descriptors of real-world objects. As optimisation tools they are also a class of computational algorithms implemented using statistical/numerical techniques for parameter estimate, model selection, and generalisation enhancement.
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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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, 2003 textabstractThe flexibility of neural networks to handle complex data patterns of economic variables is well known. In this survey we present a brief introduction to a neural network and focus on two aspects of its flexibility . First, a neural network is used to recover the dynamic properties of a nonlinear system, in particular, its stability by ...
Kaashoek, J.F., van Dijk, H.K.
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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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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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