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Convolutional neural network

Known as: Max norm constraint, Convolutional neural networks, ConvNet 
In machine learning, a convolutional neural network (CNN, or ConvNet) is a type of feed-forward artificial neural network in which the connectivity… 
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2017
Highly Cited
2017
We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a… 
Highly Cited
2017
Highly Cited
2017
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of… 
Highly Cited
2016
Highly Cited
2016
Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting… 
Highly Cited
2016
Highly Cited
2016
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and… 
Highly Cited
2016
Highly Cited
2016
Numerous important problems can be framed as learning from graph data. We propose a framework for learning convolutional neural… 
Highly Cited
2015
Highly Cited
2015
MatConvNet is an open source implementation of Convolutional Neural Networks (CNNs) with a deep integration in the MATLAB… 
Highly Cited
2014
Highly Cited
2014
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for… 
Highly Cited
2014
Highly Cited
2014
The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture… 
Highly Cited
2014
Highly Cited
2014
Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems… 
Highly Cited
2012
Highly Cited
2012
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC…