Convolution

Known as: Convolve, Carson's integral, Continuous-time convolution 
In mathematics (and, in particular, functional analysis) convolution is a mathematical operation on two functions (f and g); it produces a third… (More)
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Papers overview

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Highly Cited
2018
Highly Cited
2018
In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are… (More)
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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… (More)
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Highly Cited
2016
Highly Cited
2016
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In… (More)
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Highly Cited
2015
Highly Cited
2015
We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks… (More)
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Highly Cited
2015
Highly Cited
2015
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for… (More)
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Highly Cited
2014
Highly Cited
2014
Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating… (More)
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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… (More)
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Highly Cited
2003
Highly Cited
2003
Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a… (More)
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Highly Cited
2001
Highly Cited
2001
We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being… (More)
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Highly Cited
1981
Highly Cited
1981
Absfrucf-Cubic convolution interpolation is a new technique for resampling discrete data. It has a number of desirable features… (More)
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