Results 11 to 20 of about 53,606 (313)
Preserving gauge invariance in neural networks [PDF]
EPJ Web of Conferences, 2022 In these proceedings we present lattice gauge equivariant convolutional neural networks (L-CNNs) which are able to process data from lattice gauge theory simulations while exactly preserving gauge symmetry.
Favoni Matteo +3 more
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Video‐based action recognition using spurious‐3D residual attention networks
IET Image Processing, 2022 Recently, 3D Convolutional Neural Networks (3D CNNs) have attracted extensive attention in extracting spatial and temporal features in videos for their efficient feature extraction ability.
Bo Chen +4 more
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Performance Comparison of CNN Models Using Gradient Flow Analysis
Informatics, 2021 Convolutional neural networks (CNNs) are widely used among the various deep learning techniques available because of their superior performance in the fields of computer vision and natural language processing.
Seol-Hyun Noh
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Applications of Lattice Gauge Equivariant Neural Networks [PDF]
EPJ Web of Conferences, 2022 The introduction of relevant physical information into neural network architectures has become a widely used and successful strategy for improving their performance.
Favoni Matteo +2 more
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Research on Lane Occupancy Rate Forecasting Based on the Capsule Network
IEEE Access, 2020 This paper proposes a hybrid lane occupancy rate prediction model called 2LayersCapsNet, which combines the improved capsule network and convolutional neural networks (CNNs). The model uses CNNs to mine the spatial-temporal correlation characteristics of
Ran Tian +3 more
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The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments [PDF]
EPJ Web of Conferences, 2020 The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments.
Ayyar Venkitesh +4 more
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IET Computer Vision, 2023 High‐performance convolutional neural networks (CNNs) stack many convolutional layers to obtain powerful feature extraction capability, which leads to huge storing and computational costs.
Chengcheng Zhong +4 more
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Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations
IEEE Access, 2021 Despite various optical realizations of convolutional neural networks (CNNs), optical implementation of nonlinear activation functions and pooling operations are still challenging problems.
Hoda Sadeghzadeh +2 more
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Emotion Recognition Using Convolutional Neural Network (CNN)
Journal of Physics: Conference Series, 2021 Abstract Emotion is an expression that human use in expressing their feelings. It can be express through facial expression, body language and voice tone. Humans’ facial expression is a major way in conveying emotion since it is the most powerful, natural and universal signal to express humans’ emotion condition.
Nur Alia Syahirah Badrulhisham +1 more
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Informatics in Medicine Unlocked, 2021 Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets, networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training ...
Saman Motamed +2 more
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