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Review

2020

Review

2020

With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex… Expand

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Review

2019

Review

2019

Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix… Expand

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Review

2018

Review

2018

In big data fields, with increasing computing capability, artificial neural networks have shown great strength in solving data… Expand

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Review

2018

Review

2018

In this paper, we propose RNN-Capsule, a capsule model based on Recurrent Neural Network (RNN) for sentiment analysis. For a… Expand

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Review

2018

Review

2018

Low-rank modeling plays a pivotal role in signal processing and machine learning, with applications ranging from collaborative… Expand

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Highly Cited

2017

Highly Cited

2017

The stochastic gradient descent (SGD) method and its variants are algorithms of choice for many Deep Learning tasks. These… Expand

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Highly Cited

2009

Highly Cited

2009

The attachment of r e p l i c a t i n g DNA to a rap id ly sediment ing nuc lear s t ruc tu r e was inves t iga t ed by d iges t… Expand

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Highly Cited

2005

Highly Cited

2005

This paper considers a natural error correcting problem with real valued input/output. We wish to recover an input vector f/spl… Expand

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Highly Cited

2005

Highly Cited

2005

Suppose we wish to recover a vector x_0 Є R^m (e.g., a digital signal or image) from incomplete and contaminated observations y… Expand

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Highly Cited

2000

Highly Cited

2000

We introduce a new class of problems called network information flow which is inspired by computer network applications. Consider… Expand

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