Parallel distributed processing model with local space-invariant interconnections and its optical architecture

Zhang, Wei; Itoh, Kazuyoshi; Tanida, Jun; Ichioka, Yoshiki

doi:10.1364/ao.29.004790

Cited by 197 publications

(98 citation statements)

References 9 publications

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“…A parallel study of Zhang et al . [6] used a shift-invariant artificial neural network (SIANN) to recognize characters from an image. However, due to the lack of large training data and computing power at that time, their networks can not perform well on more complex problems, e.g., large-scale image and video classification.…”

Section: Introductionmentioning

confidence: 99%

Recent advances in convolutional neural networks

Wang

Kuen

et al. 2018

Pattern Recognition

4,749

1,923

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In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Leveraging on the rapid growth in the amount of the annotated data and the great improvements in the strengths of graphics processor units, the research on convolutional neural networks has been emerged swiftly and achieved stateof-the-art results on various tasks. In this paper, we provide a broad survey of the recent advances in convolutional neural networks. We detailize the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation. Besides, we also introduce various applications of convolutional neural networks in computer vision, speech and natural language processing.

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Section: Introductionmentioning

confidence: 99%

Recent advances in convolutional neural networks

Wang

Kuen

et al. 2018

Pattern Recognition

4,749

1,923

View full text Add to dashboard Cite

show abstract

“…It is a deformation of multilayer perceptron, mainly used to reduce the image preprocessing work. Such networks are characterized by weight sharing and image translation invariance [33,34].…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

Supervised Deep Learning in High Energy Phenomenology: a Mini Review*

Abdughani¹,

Ren²,

Wu³

et al. 2019

Commun. Theor. Phys.

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Deep learning, a branch of machine learning, have been recently applied to high energy experimental and phenomenological studies. In this note we give a brief review on those applications using supervised deep learning. We first describe various learning models and then recapitulate their applications to high energy phenomenological studies. Some detailed applications are delineated in details, including the machine learning scan in the analysis of new physics parameter space, the graph neural networks in the search of top-squark production and in the CP measurement of the top-Higgs coupling at the LHC.

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“…1 . It consists of three sections: preprocessing, processing by using the shift-invariant neural network [6][7][8], and postprocessing. In the preprocessing stage, original chest images are shrunk in order to decrease the computation time.…”

Section: Algorithm For Detection Of Lung Structuresmentioning

confidence: 99%