Results 51 to 60 of about 204,781 (309)
IEEE Access, 2020 To solve the problem of data sparsity in recommendation systems, this paper proposes a probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item auxiliary information.
Chenkun Zhang, Cheng Wang
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Scientific Reports, 2021 Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is much ...
Dimitrios Bellos +3 more
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Hyper-flexible Convolutional Neural Networks based on Generalized Lehmer and Power Means [PDF]
, 2022 Convolutional Neural Network is one of the famous members of the deep learning family of neural network architectures, which is used for many purposes, including image classification.
Branytskyi, Vladyslav +3 more
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CONVOLUTIONAL DEEP LEARNING NEURAL NETWORK FOR STROKE IMAGE RECOGNITION: REVIEW
Вестник КазНУ. Серия математика, механика, информатика, 2021 Deep learning is one of the developing area of articial intelligence research. It includes machine learning methods based on articial neural networks. One method that has been widely used and researched in recent years is convolution neural networks (CNN)
Azhar Toilybaikyzy Tursynova +3 more
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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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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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Compressing Convolutional Neural Networks
CoRR, 2015 Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers.
Wenlin Chen +4 more
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Normalized Convolutional Neural Network
CoRR, 2020 We introduce a Normalized Convolutional Neural Layer, a novel approach to normalization in convolutional networks. Unlike conventional methods, this layer normalizes the rows of the im2col matrix during convolution, making it inherently adaptive to sliced inputs and better aligned with kernel structures. This distinctive approach differentiates it from
Dongsuk Kim +4 more
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An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks
IEEE AccessRecent advancements in quantum machine learning have spurred the development of hybrid quantum-classical convolutional neural networks (HQCCNNs), which have demonstrated promising potential for image classification tasks.
Li Hai +4 more
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Application of shallow and deep convolutional neural networks to recognize the average flow rate of physiological fluids in a capillary [PDF]
, 2022 The aim of this work is to develop practical tools to recognize the average flow rate of physiological fluids in capillaries. This tool is represented by classification models in an artificial neural networks form.
Kornaeva, Elena +3 more
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