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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...)  
 
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  1-10 of 85 Reviews about "Neural Nets (C.1.3...)": Date Reviewed
  Automated machine learning: methods, systems, challenges
Hutter F., Kotthoff L., Vanschoren J.,  Springer International Publishing, New York, NY, 2019. 219 pp. Type: Book (978-3-030053-17-8)

A dataset, generically speaking, is a collection of numbers. It constitutes an information element when structure and context are assigned. The objective of machine learning (ML) is to interpret an information element as a human would. This requir...

Jun 14 2021
  Deep learning applications for cyber security
Alazab M., Tang M.,  Springer International Publishing, New York, NY, 2019. 246 pp. Type: Book (978-3-030130-56-5)

Research in the field of artificial intelligence (AI) provides new information about deep neural network (DNN)-based methods, also known as deep learning. This allows computational models, composed of countless processing layers, to learn represen...

May 7 2021
  Deep learning for NLP and speech recognition
Kamath U., Liu J., Whitaker J.,  Springer International Publishing, New York, NY, 2019. 621 pp. Type: Book (978-3-030145-95-8)

As has happened in other applied domains, deep learning has revolutionized natural language processing (NLP) systems, from speech recognition to neural machine translation. In 2017, Yoav Goldberg provided an easy-to-read overview of the (then) cur...

Dec 22 2020
   BigDL: a distributed deep learning framework for big data
Dai J., Wang Y., Qiu X., Ding D., Zhang Y., Wang Y., Jia X., Zhang C., Wan Y., Li Z., Wang J., Huang S., Wu Z., Wang Y., Yang Y., She B., Shi D., Lu Q., Huang K., Song G.  SoCC 2019 (Proceedings of the ACM Symposium on Cloud Computing, Santa Cruz, CA,  Nov 20-23, 2019) 50-60, 2019. Type: Proceedings

The sheer complexity of storage and processing methods deployed by big data applications has resulted in a prolonged wait for a deep learning framework that effectively works with big data. This paper announces the end of this wait, explaining the...

Nov 5 2020
  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 over the pas...

Sep 29 2020
  A survey on deep neural network-based image captioning
Liu X., Xu Q., Wang N.  The Visual Computer 35(3): 445-470, 2019. Type: Article, Reviews: (2 of 2)

Image captioning is an intriguing problem in the field of computer vision: given an input image, come up with suitable concise text that verbalizes that image well. This is currently a hot topic in the context of image understanding, and leverages...

Jun 29 2020
  DeepTest: automated testing of deep-neural-network-driven autonomous cars
Tian Y., Pei K., Jana S., Ray B.  ICSE 2018 (Proceedings of the 40th International Conference on Software Engineering, Gothenburg, Sweden,  May 27-Jun 3, 2018) 303-314, 2018. Type: Proceedings

A very promising and well-argued account, this paper presents a novel approach to systematically testing and automatically detecting erroneous behaviors in deep neural network (DNN)-driven vehicles....

Jun 23 2020
  Concepts of soft computing: fuzzy and ANN with programming
Chakraverty S., Sahoo D., Mahato N.,  Springer International Publishing, New York, NY, 2019. 195 pp. Type: Book

Soft computing, a branch of artificial intelligence (AI), deals with computations involving uncertainty in complex real-world problems (as opposed to hard computing, which deals with computations involving certainty only encountered in ideal situa...

Apr 29 2020
  Overcoming security vulnerabilities in deep learning-based indoor localization frameworks on mobile devices
Tiku S., Pasricha S.  ACM Transactions on Embedded Computing Systems 18(6): 1-24, 2019. Type: Article

This paper analyzes “the vulnerability of a convolutional neural network (CNN)–based indoor localization solution.” The authors “propose a novel methodology to maintain indoor localization accuracy ... in the presence of access p...

Apr 22 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 (NLP), which...

Mar 5 2020
 
 
 
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