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Academic Articles & Research Papers
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Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma et al. • Pattern Recognition
(2017)
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Open Access
DOI: https://doi.org/10.1016/j.patcog.2017.10.013
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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Louis A. Wehenkel • Springer eBooks
(1998)
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DOI: https://doi.org/10.1007/978-1-4615-5451-6_4
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami et al. • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
(2019)
Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This...
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Open Access
DOI: https://doi.org/10.1145/3292500.3330961
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
D.R. Baughman, Ying Liu • Elsevier eBooks
(1995)
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DOI: https://doi.org/10.1016/b978-0-12-083030-5.50007-2
Donald F. Specht • IEEE Transactions on Neural Networks
(1991)
A memory-based network that provides estimates of continuous variables and converges to the underlying (linear or nonlinear) regression surface is described. The general regression neural network (GRNN) is a one-pass learning algorithm with...
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DOI: https://doi.org/10.1109/72.97934
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
M.T. Hagan, H.B. Demuth • Proceedings of the ... American Control Conference/Proceedings of the American Control Conference
(1999)
Provides a quick overview of neural networks and explains how they can be used in control systems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation. The backpropagation algorit...
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DOI: https://doi.org/10.1109/acc.1999.786109
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele et al. • arXiv (Cornell University)
(2018)
In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. Therefore, it is crucial that domain specialists can understand...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1808.04260
Nicholas Carlini, David Wagner • IEEE Symposium on Security and Privacy (S&P)
(2017)
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x'...
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Open Access
DOI: https://doi.org/10.1109/sp.2017.49
Myoung-Jong Kim, Dae-Ki Kang • Expert Systems with Applications
(2009)
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DOI: https://doi.org/10.1016/j.eswa.2009.10.012
Guangyu Robert Yang, Xiao‐Jing Wang • Neuron
(2020)
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Open Access
DOI: https://doi.org/10.1016/j.neuron.2020.09.005
PMC: 11576090
Vojtěch Dvořák • Knowledge-Based Systems
(1993)
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DOI: https://doi.org/10.1016/0950-7051(93)90043-s
Peter A. Getting • Annual Review of Neuroscience
(1989)
ical, and computational techniques have provided the tools to begin uncovering concepts underlying neural network function. This review is a summary of insights into the organization and operation of neural circuits acquired through applica...
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DOI: https://doi.org/10.1146/annurev.ne.12.030189.001153
Mark W. Craven, JUDE W. SHAVLIK • Future Generation Computer Systems
(1997)
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DOI: https://doi.org/10.1016/s0167-739x(97)00022-8
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
Stefano Zambelli • Journal of Economic Dynamics and Control
(1993)
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DOI: https://doi.org/10.1016/0165-1889(93)90010-p
Gene A. Tagliarini, John Fury Christ, Edward W. Page • IEEE Transactions on Computers
(1991)
The design of feedback (or recurrent) neural networks to produce good solutions to complex optimization problems is discussed. The theoretical basis for applying neural networks to optimization problems is reviewed, and a design rule that s...
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DOI: https://doi.org/10.1109/12.106220
Ke-Lin Du, M. N. S. Swamy • Springer eBooks
(2019)
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DOI: https://doi.org/10.1007/978-1-4471-7452-3