Convolutional neural network · Artificial neural network · YYNN

Convolutional neural network

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Artificial neural network

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Weakly-supervised convolutional neural networks for multimodal image registration

scholarly article by Yipeng Hu et al published October 2018 in Medical Image Analysis

Convolutional Neural Networks for Pediatric Refractory Epilepsy Classification Using Resting-State fMRI

scientific article published on 05 January 2021

Automatic Brain Tumor Segmentation Based on Cascaded Convolutional Neural Networks With Uncertainty Estimation

scientific article published on 13 August 2019

Multiple Convolutional Recurrent Neural Networks for Fault Identification and Performance Degradation Evaluation of High-Speed Train Bogie

scientific article published on 10 February 2020

Predicting CO2 Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network

scientific article published in July 2019

Enhancing Audio Classification Through MFCC Feature Extraction and Data Augmentation with CNN and RNN Models

scientific article published on 31 July 2024

Exploiting the Use of Convolutional Neural Networks for Localization in Indoor Environments

scholarly article by Bruno V. Ferreira et al published 12 May 2017 in Applied Artificial Intelligence

Fully Automatic Atrial Fibrosis Assessment Using a Multilabel Convolutional Neural Network

scientific article published on 15 December 2020

Convolutional neural networks for transient candidate vetting in large-scale surveys

scholarly article by Fabian Gieseke et al published 23 August 2017 in Monthly Notices of the Royal Astronomical Society

Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks

scientific article published on 20 March 2020

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A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each...

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