convolutional neural networks (CNNs)
OVERVIEW OF CONVOLUTIONAL NEURAL NETWORKS
... Neural networks are one of the most powerful technologies that are used for a variety of classification and prediction ...summarizes convolutional neural network which is the new buzzword in ...
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Convolutional neural networks for brain tumour segmentation
... as convolutional neural networks (CNNs) which are machine learning pipelines modelled on the biological process of neurons (called nodes) and synapses (connections) have been of interest in the ...
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Convolutional Neural Networks in Medical Imaging
... differentiate convolutional neural networks from other neural networks, the first layer of all CNNs is a convolutional ...The convolutional layer takes an array of values ...
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Understanding Convolutional Neural Networks for Text Classification
... We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text. CNNs used for computer vi- sion can be interpreted by projecting filters into image space, ...
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Notes on Convolutional Neural Networks
... of convolutional neural networks (CNNs) [3, 4], followed by a few straightforward ...extensions. Convolutional neural networks in- volve many more connections than weights; the ...
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Applications of Convolutional Neural Networks
... distortions. Convolutional neural networks were ...and convolutional neural ...convolution neural network ...a convolutional neural based text detection system was ...
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Super Sparse Convolutional Neural Networks
... of Convolutional Neural Networks (CNNs) is usually too large to be deployed on the mobile devices and they often suffer from the over-fitting problem caused by the less abundant ...the ...
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Convolutional Neural Networks for Sentence Classification
... In the present work we have described a series of experiments with convolutional neural networks built on top of word2vec. Despite little tuning of hyperparameters, a simple CNN with one layer of ...
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Using Convolutional Neural Networks for Sentiment Attitude Extraction from Analytical Texts
... The model proposed in this paper does not depends on the manually created features. This has been achieved by using the Convolutional Neural Networks (CNN) architecture which exposes filters as ...
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Deep convolutional neural networks capabilities for
... for the detection of MCs based on the use of deep convolutional neural networks (DCNNs).. We.[r] ...
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Resiliency in Deep Convolutional Neural Networks
... of Convolutional Neural Networks increased the demand for larger GPU’s, to reduce this, smaller architectures like SqueezeNet [12] started taking the place of these larger architectures which were ...
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Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics
... for the detection of MCs based on the use of deep convolutional neural networks (DCNNs).. As a 21.[r] ...
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Cyberbullying Intervention Based on Convolutional Neural Networks
... This paper describes the process of building a cyberbullying intervention interface driven by a machine-learning based text-classification service. We make two main contribu- tions. First, we show that cyberbullying can ...
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A General Purpose Tagger with Convolutional Neural Networks
... We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information. The CNN tagger is robust across different tag- ...
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Fungus Detection using Convolutional Neural Networks
... using convolutional neural networks in order to identify whether the given classes of fungus is normal or ...of convolutional neural networks (CNN)[6], we are identifying the ...
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Spam detection in im images using convolutional neural networks
... precisely what this paper intends tom illustrate. As mentioned earlier in this paper, classifying emails has been a standard classification problem for ears. But we are no longer limited to emails anymore. Spammers have ...
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Brain Tumor Classification Using Convolutional Neural Networks
... The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, ...
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Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
... Convolutional neural networks (CNN) have led to many state-of-the-art results spanning through various ...approximation, Convolutional Sparse Coding (CSC) has gained increasing attention in ...
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Sentiment Classification Via Recurrent Convolutional Neural Networks
... Recurrent Neural Network ...the Convolutional Neural Network (CNN) for sentiment ...recurrent neural networks, CNN may be more beneficial to the process of capturing text ...simple ...
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Labeling Paths with Convolutional Neural Networks
... our neural network functions only as a classifier for determining drivable paths in an ...our neural network consisting of a robot’s odometry data as it collects images while navigating the surrounding ...
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