Results 1 to 10 of about 1,925,096 (328)
Optical Diffractive Convolutional Neural Networks Implemented in an All-Optical Way [PDF]
Sensors, 2023 Optical neural networks can effectively address hardware constraints and parallel computing efficiency issues inherent in electronic neural networks. However, the inability to implement convolutional neural networks at the all-optical level remains a ...
Yaze Yu +4 more
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Convolutional Neural Networks [PDF]
, 2022 AbstractWe provide the fundamentals of convolutional neural networks (CNNs) and include several examples using the Keras library. We give a formal motivation for using CNN that clearly shows the advantages of this topology compared to feedforward networks for processing images. Several practical examples with plant breeding data are provided using CNNs
Osval Antonio Montesinos López +2 more
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Content-aware convolutional neural networks [PDF]
Neural Networks, 2021 Accepted by Neural ...
Yong Guo +5 more
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Self-grouping convolutional neural networks [PDF]
Neural Networks, 2020 Although group convolution operators are increasingly used in deep convolutional neural networks to improve the computational efficiency and to reduce the number of parameters, most existing methods construct their group convolution architectures by a predefined partitioning of the filters of each convolutional layer into multiple regular filter groups
Qingbei Guo +3 more
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Convolution-Bidirectional Temporal Convolutional Network for Protein Secondary Structure Prediction
IEEE Access, 2022 As a basic feature extraction method, convolutional neural networks have some information loss problems when dealing with sequence problems, and a temporal convolutional network can compensate for this problem.
Yunqing Zhang, Yuming Ma, Yihui Liu
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Quantum convolutional neural networks [PDF]
Nature Physics, 2019 12 pages, 11 figures. v2: New application to optimizing quantum error correction codes, added sample complexity analysis, more details for experimental realizations, and other minor ...
Iris Cong +2 more
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Improved Convolutional Neural Image Recognition Algorithm based on LeNet-5
Journal of Computer Networks and Communications, 2022 Convolutional neural network (CNN) is a very important method in deep learning, which solves many complex pattern recognition problems. Fruitful results have been achieved in image recognition, speech recognition, and natural language processing ...
Lijie Zhou, Weihai Yu
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Texture synthesis of ecological plant protection image based on convolution neural network
Frontiers in Plant Science, 2022 Texture synthesis technology is an important realistic rendering technology. Texture synthesis technology also has a good application prospect in image rendering and other fields. Convolutional neural network is a very popular technology in recent years.
Libing Hu, Fei Zhou, Xianjun Fu
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DISTRIBUTED CONVOLUTIONAL NEURAL NETWORK MODEL ON RESOURCE-CONSTRAINED CLUSTER [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2020 Subject of Research. The paper presents the distributed deep learning particularly convolutional neural network problem for resource-constrained devices.
Rezeda R. Khaydarova +3 more
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Method for predicting cutter remaining life based on multi-scale cyclic convolutional network
International Journal of Distributed Sensor Networks, 2022 In the process of predicting the remaining cutter life, the deep-learning method such as convolutional neural network does not consider the time correlation of different degradation states, which directly affects the accuracy of the remaining cutter life
Tao Li +5 more
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