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Academic Articles & Research Papers
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Richard J. Mammone (1994)
Neural Networks for speech processing. Neural Networks in the acquisition of speech by machine. The nervous system - fantasy and reality. Processing of complex stimuli in the mammalian cochlear nucleus. On the possible role of auditory peri...
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N.K. Bose, Pengfei Liang (1995)
Part 1 Fundamentals: basics of neuroscience and artificial neuron models graphs algorithms. Part 2 Feedforward networks: perceptrons and LMS algorithm complexity of learning using feedforward networks adaptive structure networks. Part 3 Rec...
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M.J. Willis, Christine Di Massimo, Gary A. Montague, Ming T. Tham et al. • IEE Proceedings D Control Theory and Applications
(1991)
Artificial neural networks are made up of highly interconnected layers of simple ‘neuron-like’ nodes. The neurons act as nonlinear processing elements within the network. An attractive property of artificial neural networks is that, given t...
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DOI: https://doi.org/10.1049/ip-d.1991.0036
Oludare Isaac Abiodun, Aman Jantan, Abiodun Esther Omolara, Kemi Victoria Dada et al. • Heliyon
(2018)
This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and ar...
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cc-by
DOI: https://doi.org/10.1016/j.heliyon.2018.e00938
PMC: 6260436
Stephan Rasp, Sebastian Lerch • Monthly Weather Review
(2018)
Abstract Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the pa...
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other-oa
DOI: https://doi.org/10.1175/mwr-d-18-0187.1
Hervé Abdi • Journal of Biological Systems
(1994)
Neural networks are composed of basic units somewhat analogous to neurons. These units are linked to each other by connections whose strength is modifiable as a result of a learning process or algorithm. Each of these units integrates indep...
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DOI: https://doi.org/10.1142/s0218339094000179
Jiahui Yu, Linjie Yang, Ning Xu, Yang, Jianchao et al. • arXiv (Cornell University)
(2018)
We present a simple and general method to train a single neural network executable at different widths (number of channels in a layer), permitting instant and adaptive accuracy-efficiency trade-offs at runtime. Instead of training individua...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1812.08928
J. T. Gene Hwang, Aidong Adam Ding • Journal of the American Statistical Association
(1997)
The artificial neural network (ANN) is becoming a very popular model for engineering and scientific applications. Inspired by brain architecture, artificial neural networks represent a class of nonlinear models capable of learning from data...
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DOI: https://doi.org/10.1080/01621459.1997.10474027
Bing Cheng, D. Michael Titterington • Statistical Science
(1994)
This paper informs a statistical readership about Artificial Neural Networks (ANNs), points out some of the links with statistical methodology and encourages cross-disciplinary research in the directions most likely to bear fruit. The areas...
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Open Access
DOI: https://doi.org/10.1214/ss/1177010638
Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia et al. • arXiv (Cornell University)
(2020)
Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet even though neural network models see increasing use in the...
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Open Access
DOI: https://doi.org/10.48550/arxiv.2003.04630
Daniel J. Amit, Hanoch Gutfreund, Haim Sompolinsky • Physical Review A
(1985)
Two dynamical models, proposed by Hopfield and Little to account for the collective behavior of neural networks, are analyzed. The long-time behavior of these models is governed by the statistical mechanics of infinite-range Ising spin-glas...
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DOI: https://doi.org/10.1103/physreva.32.1007
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
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
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
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