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	Browse All Reviews > Computer Systems Organization (C) > Processor Architectures (C.1) > Other Architecture Styles (C.1.3) > Neural Nets (C.1.3...)     Options: All Media Types Journals Proceedings Div Books Whole Books Other Date Reviewed Title Author Publisher Published Date Descending Ascending     1-10 of 29 Reviews about "Neural Nets (C.1.3...)": Date Reviewed   On the adaptation of recurrent neural networks for system identification Forgione M., Muni A., Piga D., Gallieri M. Automatica (Journal of IFAC) 1552023.  Type: Article In this pape, Forgione et al. propose a transfer learning methodology to adapt recurrent neural network (RNN) models for dynamic system identification to new operating conditions. The approach is premised on the concept that the dynamics of real-w... Apr 9 2024   Detection of crop pests and diseases based on deep convolutional neural network and improved algorithm Wu J., Li B., Wu Z.  ICMLT 2019 (Proceedings of the 2019 4th International Conference on Machine Learning Technologies, Nanchang, China, Jun 21-23, 2019) 20-27, 2019.  Type: Proceedings Especially in large monoculture-based agricultural settings, an outbreak of pests or diseases can have a major impact on yield or quality of a crop. Advances in image processing based on convolutional neural network (CNN) architecture ... Sep 29 2020   Introduction to deep learning Charniak E., The MIT Press, Cambridge, MA, 2019. 192 pp.  Type: Book (978-0-262039-51-2) Deep learning has taken many application domains by storm, specifically those where artificial intelligence (AI) techniques have been struggling without too much success for decades. One of those domains is natural language processing ... Mar 5 2020   Grokking deep learning Trask A., Manning Publications Co., Shelter Island, NY, 2019. 336 pp.  Type: Book (978-1-617293-70-2) Deep learning is a hot topic in artificial intelligence (AI). It is exciting to see a book that can help readers understand the ideas of deep learning without advanced knowledge of mathematics.... Feb 14 2020   Deep neural networks classification over encrypted data Hesamifard E., Takabi H., Ghasemi M.  CODASPY 2019 (Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy, Richardson, TX, Mar 25-27, 2019) 97-108, 2019.  Type: Proceedings When we speak about a convolutional neural network (CNN) as a more complex deep learning algorithm, there are privacy-preserving issues that could be addressed in any study on the topic. In deep learning, CNNs are used to analyze compl... Feb 13 2020   Neural network classifiers using a hardware-based approximate activation function with a hybrid stochastic multiplier Li B., Qin Y., Yuan B., Lilja D. ACM Journal on Emerging Technologies in Computing Systems 15(1): 1-21, 2019.  Type: Article Li et al. present a novel approach for optimizing neural network implementations, that is, “a new architecture of stochastic neural networks” with a hidden approximate activation function and a hybrid stochastic mul... May 1 2019   A novel multilayer AAA model for integrated applications Rezakhani A., Shirazi H., Modiri N. Neural Computing and Applications 29(10): 887-901, 2018.  Type: Article Unidimensional static security policies cannot cater to the needs of a growing enterprise anymore. Local regulations, business processes, operational levels, and threat modeling are the key anchors around which successful organizations... Mar 22 2019   Spiking deep convolutional neural networks for energy-efficient object recognition Cao Y., Chen Y., Khosla D. International Journal of Computer Vision 113(1): 54-66, 2015.  Type: Article A novel mechanism for converting convolutional neural networks (CNNs) to spiking neural networks (SNNs) to facilitate ready deployment, that is, mapping on spiking hardware architectures, is proposed in this paper.... Sep 28 2015   Classification of arrhythmia using hybrid networks Haseena H., Joseph P., Mathew A. Journal of Medical Systems 35(6): 1617-1630, 2011.  Type: Article A new methodology for medical differential diagnosis purposes, based on the use of fuzzy clustered hybrid networks, is detailed in this paper. The study is focused on discriminating among the diseases of a certain class that includes n... May 17 2012   Self-organized two-state membrane potential transitions in a network of realistically modeled cortical neurons Kang S., Kitano K., Fukai T. Neural Networks 17(3): 307-312, 2004.  Type: Article Kang, Kitano, and Fukai present us with a computational model of cortical neurons that have recently been reported (as a result of in vitro and in vivo experiments, conducted as early as 1999) to show spontaneous transitions between tw... Mar 1 2005       Display 510152550100 per page
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