Results 31 to 40 of about 3,605,315 (303)
A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging
Engineering Proceedings, 2023 Convolutional Neural Networks (CNNs) are kinds of deep learning models that were created primarily for processing and evaluating visual input, which makes them extremely applicable in the field of medical imaging.
Satyam Tiwari +5 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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Creating Deep Convolutional Neural Networks for Image Classification
The Programming Historian, 2023 This lesson provides a beginner-friendly introduction to convolutional neural networks (CNNs) for image classification. The tutorial provides a conceptual understanding of how neural networks work by using Google’s Teachable Machine to train a model on ...
Nabeel Siddiqui
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Remote Sensing, 2020 The accurate acquisition of water information from remote sensing images has become important in water resources monitoring and protections, and flooding disaster assessment.
Guojie Wang +3 more
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Quantum convolutional neural networks for high energy physics data analysis
Physical Review Research, 2022 This paper presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment.
Samuel Yen-Chi Chen +4 more
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CNN 101: Interactive Visual Learning for Convolutional Neural Networks [PDF]
Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 2020 The success of deep learning solving previously-thought hard problems has inspired many non-experts to learn and understand this exciting technology. However, it is often challenging for learners to take the first steps due to the complexity of deep learning models.
Zijie J. Wang +7 more
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Empirical Remarks on the Translational Equivariance of Convolutional Layers
Applied Sciences, 2020 In general, convolutional neural networks (CNNs) maintain some level of translational invariance. However, the convolutional layer itself is translational-equivariant. The pooling layers provide some level of invariance. In object recognition, invariance
Kyung Joo Cheoi +2 more
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A Review of Convolutional Neural Network Development in Computer Vision
EAI Endorsed Transactions on Internet of Things, 2022 Convolutional neural networks have made admirable progress in computer vision. As a fast-growing computer field, CNNs are one of the classical and widely used network structures. The Internet of Things (IoT) has gotten a lot of attention in recent years.
Hang Zhang
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Predicting the Demand in Bitcoin Using Data Charts: A Convolutional Neural Networks Prediction Model
, 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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Canonical convolutional neural networks
, 2023 33983405We introduce canonical weight normalization for convolutional neural networks. Inspired by the canonical tensor decomposition, we express the weight tensors in so-called canonical networks as scaled sums of outer vector products.
Wolter, Moritz +3 more
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