Home »
Academic Articles & Research Papers
Showing 20 of 4,567 academic articles
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...
OPENALEX
DOI: https://doi.org/10.1080/01621459.1997.10474027
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...
OPENALEX
DOI: https://doi.org/10.1103/physreva.32.1007
Leon Ong Chua, Lei Yang • IEEE International Symposium on Circuits and Systems proceedings
(2003)
A novel class of information-processing systems called cellular neural networks is proposed. Like neural networks, they are large-scale nonlinear circuits which process signals in real time. Like cellular automata, they are made of a massiv...
OPENALEX
DOI: https://doi.org/10.1109/iscas.1988.15089
Leon Ong Chua, L. Yang • IEEE Transactions on Circuits and Systems
(1988)
The theory of a novel class of information-processing systems, called cellular neural networks, which are capable of high-speed parallel signal processing, was presented in a previous paper (see ibid., vol.35, no.10, p.1257-72, 1988). A dyn...
OPENALEX
DOI: https://doi.org/10.1109/31.7601
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...
OPENALEX
DOI: https://doi.org/10.1049/pbce053e_ch1
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...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1509.06569
Leon Ong Chua, L. Yang • IEEE Transactions on Circuits and Systems
(1988)
A novel class of information-processing systems called cellular neural networks is proposed. Like neural networks, they are large-scale nonlinear analog circuits that process signals in real time. Like cellular automata, they consist of a m...
OPENALEX
DOI: https://doi.org/10.1109/31.7600
Dana H. Ballard • National Conference on Artificial Intelligence
(1987)
In the development of large-scale knowledge networks much recent progress has been inspired by connections to neurobiology. An important component of any neural network is an accompanying learning algorithm. Such an algorithm, to be biologi...
OPENALEX
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 ...
OPENALEX
cc-by
DOI: https://doi.org/10.58496/mjcsc/2023/015
Stamatios V. Kartalopoulos • Institute of Electrical and Electronics Engineers eBooks
(1995)
Understand the fundamentals of the emerging field of fuzzy neural networks, their applications and the most used paradigms with this carefully organized state-of-the-art textbook. Previously tested at a number of noteworthy conference tutor...
OPENALEX
DOI: https://doi.org/10.1109/9780470546826
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 ...
OPENALEX
DOI: https://doi.org/10.1109/iecon.1995.483329
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...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1906.01563
• The MIT Press eBooks
(1991)
Neural Networks for Control highlights key issues in learning control and identifies research directions that could lead to practical solutions for control problems in critical application domains. It addresses general issues of neural netw...
OPENALEX
DOI: https://doi.org/10.7551/mitpress/4939.001.0001
David E. Rumelhart, Bernard Carlos Widrow, Michael A. Lehr • Communications of the ACM
(1994)
article Free AccessThe basic ideas in neural networks Authors: David E. Rumelhart Stanford Univ., Stanford, CA Stanford Univ., Stanford, CAView Profile , Bernard Widrow Stanford Univ., Stanford, CA Stanford Univ., Stanford, CAView Profile ,...
OPENALEX
Open Access
DOI: https://doi.org/10.1145/175247.175256
• The MIT Press eBooks
(1998)
Most practical applications of artificial neural networks are based on a computational model involving the propagation of continuous variables from one processing unit to the next. In recent years, data from neurobiological experiments have...
OPENALEX
DOI: https://doi.org/10.7551/mitpress/5704.001.0001
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Cohen, Jonathan et al. • arXiv (Cornell University)
(2014)
We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and time-consuming. As parallel architectures evolve, kernels mu...
OPENALEX
Open Access
DOI: https://doi.org/10.48550/arxiv.1410.0759
Umberto Michelucci • Apress eBooks
(2018)
Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks, in this compr...
OPENALEX
Open Access
DOI: https://doi.org/10.1007/978-1-4842-3790-8
Sašo Džeroski, Luc De Raedt, Stefan Wrobel (2005)
This volume, which is also available online from http://www.machinelearning.org, contains the papers accepted for presentation at ICML-2005, the 22nd lnternational Conference on Machine Learning, which was held at the University of Bonn in ...
OPENALEX
LIN Yan, JIN Tingyu, YANG Yuchao • Shanghai Jiaotong Daxue xuebao
(2024)
Aimed at the problem of low efficiency of ship pipeline design, an optimization method of pipeline layout is proposed. An optimization mathematical model is established by comprehensively considering the engineering background of safety, ec...
DOAJ
Open Access (DOAJ)
DOI: 10.16183/j.cnki.jsjtu.2022.508
Karen I. Stocks, Sam Schramski, Arika Virapongse, Lisa Kempler • Data Science Journal
(2019)
Cyberinfrastructure (CI) is a standard tool in the geosciences, but the creation of successful CI remains difficult, and expensive projects can have significant consequences for scientific communities if they do not result in success. In th...
DOAJ
Open Access (DOAJ)
DOI: 10.5334/dsj-2019-021