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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… 
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Papers overview

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Highly Cited
2018
Highly Cited
2018
Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The… 
Highly Cited
2018
Highly Cited
2018
Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over… 
Highly Cited
2017
Highly Cited
2017
In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the… 
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
2015
Highly Cited
2015
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for… 
Highly Cited
2015
Highly Cited
2015
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition… 
Highly Cited
2015
Highly Cited
2015
We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks… 
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… 
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… 
Review
1999
Review
1999
We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs…