Results 11 to 20 of about 73,468 (309)
Deep Learning: Basics and Convolutional Neural Networks (CNNs)
, 2023 Abstract Deep learning belongs to the broader family of machine learning methods and currently provides state-of-the-art performance in a variety of fields, including medical applications. Deep learning architectures can be categorized into different groups depending on their components. However, most of them share similar modules and
Vakalopoulou M +4 more
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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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Predicting the Demand in Bitcoin Using Data Charts: A Convolutional Neural Networks Prediction Model [PDF]
, 2020 Traditional time series modeling techniques emphasize on predicting cryptocurrencies using classically structured data representation as numerical features to present the time-series datasets.
Kashef, R., Corrigan, L., Ibrahim, A.
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Understanding convolutional neural networks [PDF]
, 2021 In the past decade, deep learning has fueled a number of exciting developments in artificial intelligence (AI). However, as deep learning is increasingly being applied to high-impact domains, like medical diagnosis or autonomous driving, the impact of ...
Fong, Ruth
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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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Convolutional neural networks with dynamic regularization [PDF]
, 2020 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 ...
Wang, Yi +3 more
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Ensemble of Convolutional Neural Networks to diagnose Acute Lymphoblastic Leukemia from microscopic images [PDF]
, 2021 Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer characterized by the presence of excess immature lymphocytes., Even though automation in ALL prognosis is essential for cancer diagnosis, it remains a challenge due to the morphological ...
Islam, Md Rabiul +7 more
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Imaging from temporal data via spiking convolutional neural networks [PDF]
, 2020 A new approach for imaging that is solely based on the time of flight of photons coming from the entire imaged scene, combined with a novel machine learning algorithm for image reconstruction: a spiking convolutional neural network (SCNN) named Spike-SPI
Kapitany, Valentin +6 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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