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Review

2018

Review

2018

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data… Expand

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Review

2017

Review

2017

The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We… Expand

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Review

2017

Review

2017

Generative adversarial networks (GANs) have given us a great tool to fit implicit generative models to data. Implicit… Expand

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Review

2016

Review

2016

In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning… Expand

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Review

2016

Review

2016

ABSTRACTOne of the core problems of modern statistics is to approximate difficult-to-compute probability densities. This problem… Expand

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Review

2016

Review

2016

Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP… Expand

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Highly Cited

2014

Highly Cited

2014

Abstract: How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous… Expand

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Highly Cited

2008

Highly Cited

2008

The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random… Expand

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Highly Cited

2003

Highly Cited

2003

Newton Methods for Nonsmooth Equations.- Global Methods for Nonsmooth Equations.- Equation-Based Algorithms for Complementarity… Expand

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Highly Cited

1974

Highly Cited

1974

The variational principle states that if a differentiable functional F attains its minimum at some point zi, then F’(C) = 0; it… Expand

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