Results 51 to 60 of about 1,718,101 (355)
Convolutional Graph Neural Networks
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019 Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit the regular structure of time signals and images.
Fernando Gama +3 more
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Convolutional Neural Networks With Dynamic Regularization [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Regularization is commonly used for alleviating overfitting in machine learning. For convolutional neural networks (CNNs), regularization methods, such as DropBlock and Shake-Shake, have illustrated the improvement in the generalization performance. However, these methods lack a self-adaptive ability throughout training.
Yi Wang 0068 +3 more
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A Graph-Convolutional Neural Network for Addressing Small-Scale Reaction Prediction
, 2021 We describe a graph-convolutional neural network (GCN) model whose reaction prediction capable as potent as the transformer model on sufficient data, and adopt the Baeyer-Villiger oxidation to explore their performance differences on limited data.
Yejian, Wu +3 more
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Diagnostics, 2022 Background: Otitis media includes several common inflammatory conditions of the middle ear that can have severe complications if left untreated. Correctly identifying otitis media can be difficult and a screening system supported by machine learning ...
Josefin Sandström +4 more
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Convolutional neural networks: an overview and application in radiology
Insights into Imaging, 2018 Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology.
R. Yamashita +3 more
semanticscholar +1 more source
Pointwise Convolutional Neural Networks [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 10 pages, 6 figures, 10 tables.
Binh-Son Hua +2 more
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Cloud-based video analytics using convolutional neural networks. [PDF]
, 2018 Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management.
Anjum, Ashiq +3 more
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SC-PNN: Saliency Cascade Convolutional Neural Network for Pansharpening
, 2021 In many remote sensing tasks, different types of regions or targets differ in requirements for spectral and spatial quality. The discrepancy reveals that a uniform pansharpening strategy applying to the entire image may not fulfill the varying demands of
Zhang, Jue +3 more
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Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network [PDF]
IEEE transactions on circuits and systems for video technology (Print), 2019 We propose a deep bilinear model for blind image quality assessment that works for both synthetically and authentically distorted images. Our model constitutes two streams of deep convolutional neural networks (CNNs), specializing in two distortion ...
Weixia Zhang +4 more
semanticscholar +1 more source
Convolutional Neural Networks In Convolution
CoRR, 2018 Currently, increasingly deeper neural networks have been applied to improve their accuracy. In contrast, We propose a novel wider Convolutional Neural Networks (CNN) architecture, motivated by the Multi-column Deep Neural Networks and the Network In Network(NIN), aiming for higher accuracy without input data transmutation.
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