Convolutional neural networks - Open Access .click

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An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks

IEEE Access
Recent 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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Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models

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
doaj +1 more source

Integrating convolutional neural networks into a sparse distributed representation model based on mammalian cortical learning

, 2016
Biological brains exhibit a remarkable capacity to recognise real-world patterns effectively. Despite major advances in neuroscience over the last few decades, an understanding of the brain's underlying mechanisms for pattern recognition remains ...
Daniel E. Padilla +3 more
core +1 more source

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
openaire +4 more sources

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, Soonwon Choi, Mikhail D. Lukin +2 more
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Dual-channel deep graph convolutional neural networks

Frontiers in Artificial Intelligence
The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks.
Zhonglin Ye +15 more
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Factorial Convolution Neural Networks

CoRR, 2021
In recent years, GoogleNet has garnered substantial attention as one of the base convolutional neural networks (CNNs) to extract visual features for object detection. However, it experiences challenges of contaminated deep features when concatenating elements with different properties.
Jaemo Sung, Eun-Sung Jung
openaire +3 more sources

Quantum Dilated Convolutional Neural Networks

IEEE Access, 2022
In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elements ...
Yixiong Chen
doaj +1 more source

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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cemac/LIFD_ConvolutionalNeuralNetworks: LIFD Convolutional Neural Networks

, 2022
LIFD Convolutional Neural Networks ...
Helen Burns
core +1 more source

convolutional neural network
deep learning
machine learning

image classification