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Showing 20 of 3,783 academic articles
Kevin Warwick • Institution of Engineering and Technology eBooks
(1995)
In this chapter, neural networks are discussed. The present field of neural networks links a number of closely related areas, such as parallel distributed processing, connectionism and neural computing, these being brought together with the...
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DOI: https://doi.org/10.1049/pbce053e_ch1
Chris Bishop • Choice Reviews Online
(1994)
From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probabi...
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DOI: https://doi.org/10.5860/choice.31-5500
Tim P. Vogels, Kanaka Rajan, Larry F. Abbott • Annual Review of Neuroscience
(2005)
Neural network modeling is often concerned with stimulus-driven responses, but most of the activity in the brain is internally generated. Here, we review network models of internally generated activity, focusing on three types of network dy...
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DOI: https://doi.org/10.1146/annurev.neuro.28.061604.135637
Allan M. Pinkus • Acta Numerica
(1999)
In this survey we discuss various approximation-theoretic problems that arise in the multilayer feedforward perceptron (MLP) model in neural networks. The MLP model is one of the more popular and practical of the many neural network models....
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DOI: https://doi.org/10.1017/s0962492900002919
Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals • arXiv (Cornell University)
(2014)
We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1409.2329
Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry Vetrov • arXiv (Cornell University)
(2015)
Deep neural networks currently demonstrate state-of-the-art performance in several domains. At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required b...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1509.06569
José M. Benítez, Juan Luis Castro, Ignacio Requena • IEEE Transactions on Neural Networks
(1997)
Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Notwithstanding, one of the major criti...
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DOI: https://doi.org/10.1109/72.623216
Hecht-Nielsen • International Joint Conference on Neural Networks (IJCNN)
(1989)
The author presents a survey of the basic theory of the backpropagation neural network architecture covering architectural design, performance measurement, function approximation capability, and learning. The survey includes previously know...
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DOI: https://doi.org/10.1109/ijcnn.1989.118638
Roheen Qamar, Baqar Ali Zardari • Mesopotamian Journal of Computer Science
(2023)
Neural networks, also known as artificial neural networks (ANNs) or artificially generated neural networks (SNNs) are a subset of machine learning that provide the foundation of deep learning techniques. Their name and form are inspired by ...
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cc-by
DOI: https://doi.org/10.58496/mjcsc/2023/015
Chris Bishop (1995)
Abstract This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes technique...
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DOI: https://doi.org/10.1093/oso/9780198538493.001.0001
Amey Thakur • International Journal for Research in Applied Science and Engineering Technology
(2021)
The purpose of this study is to familiarise the reader with the foundations of neural networks. Artificial Neural Networks (ANNs) are algorithm-based systems that are modelled after Biological Neural Networks (BNNs). Neural networks are an ...
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Open Access
DOI: https://doi.org/10.22214/ijraset.2021.37362
Raban Iten, Tony Metger, Henrik Wilming, Lídia del Rio et al. • Physical Review Letters
(2020)
Despite the success of neural networks at solving concrete physics problems, their use as a general-purpose tool for scientific discovery is still in its infancy. Here, we approach this problem by modeling a neural network architecture afte...
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Open Access
DOI: https://doi.org/10.1103/physrevlett.124.010508
Hojjat Adeli • Computer-Aided Civil and Infrastructure Engineering
(2001)
The first journal article on neural network application in civil/structural engineering was published by in this journal in 1989. This article reviews neural network articles published in archival research journals since then. The emphasis ...
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cc-by-nc-nd
DOI: https://doi.org/10.1111/0885-9507.00219
Hal S. Stern • Technometrics
(1996)
Artificial neural networks are computer algorithms or computer programs derived in part from attempts to model the activity of nerve cells. They have been applied to pattern recognition, classification, and optimization problems in the phys...
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DOI: https://doi.org/10.1080/00401706.1996.10484497
Brad Warner, Manavendra Misra • The American Statistician
(1996)
Neural networks have received a great deal of attention over the last few years. They are being used in the areas of prediction and classification, areas where regression models and other related statistical techniques have traditionally be...
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DOI: https://doi.org/10.1080/00031305.1996.10473554
Paul Refenes • Wiley eBooks
(1995)
PART ONE: NEURAL NETWORKS: Introduction Design Considerations Methods for Optimal Network Design Data Modelling Considerations Testing Strategies and Metrics PART TWO: EQUITY APPLICATIONS: Modelling Stock Returns in the Framework of APT: A ...
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Robert E. Uhrig • Annual Conference of the IEEE Industrial Electronics Society (IECON)
(2002)
A neural network is a data processing system consisting of a large number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks ...
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DOI: https://doi.org/10.1109/iecon.1995.483329
Abhay B. Bulsari • Elsevier eBooks
(1995)
Part 1 Theoretical issues: an introduction to artificial neural networks, D.T. Pham unsupervised neural learning, E. Oja back-propagation and its variations, D. Tsaptsinos the general approximation problem for feed-forward neural networks, ...
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Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna et al. • arXiv (Cornell University)
(2013)
Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpr...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1312.6199
Sam Greydanus, Misko Dzamba, Jason Yosinski • arXiv (Cornell University)
(2019)
Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? In this paper, we draw inspiration from Hamiltonian mechanics to train models th...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1906.01563