Residual

Loosely speaking, a residual is the error in a result. To be precise, suppose we want to find x such that Given an approximation x0 of x, the… (More)
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Topic mentions per year

Topic mentions per year

1937-2018
010002000300019372017

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2017
Highly Cited
2017
Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One… (More)
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Highly Cited
2017
Highly Cited
2017
We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a… (More)
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Highly Cited
2016
Highly Cited
2016
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks… (More)
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Highly Cited
2016
Highly Cited
2016
Deep residual networks [1] have emerged as a family of extremely deep architectures showing compelling accuracy and nice… (More)
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Highly Cited
2016
Highly Cited
2016
Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However… (More)
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Highly Cited
2007
Highly Cited
2007
The ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational… (More)
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Highly Cited
2002
Highly Cited
2002
A computer system is described in which isolated words, spoken by a designated talker, are recognized through calculation of a… (More)
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Highly Cited
1995
Highly Cited
1995
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to the optimal solution when… (More)
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Highly Cited
1994
Highly Cited
1994
We propose a new method for estimation in linear models. The \lasso" minimizes the residual sum of squares subject to the sum of… (More)
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Highly Cited
1991
Highly Cited
1991
The biconjugate gradient (BCG) method is the \natural" generalization of the classical conjugate gradient algorithm for Hermitian… (More)
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