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KERNEL METHODS MATCH DEEP NEURAL NETWORKS ON TIMIT Po-Sen Huang† , Haim Avron‡ , Tara N. Sainath‡ , Vikas Sindhwani‡ , Bhuvana Ramabhadran‡ † Department of Electrical and Computer Engineering, University of I
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Document Date: 2014-03-11 17:55:13

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City

Urbana-Champaign / /

Company

T. Sarl / IBM / Neural Information Processing Systems / Air Force Research Laboratory / GPU / Deep Neural Networks / /

Country

United States / /

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Facility

University of Illinois / /

IndustryTerm

convex networks / optimization algorithms / inner product / distributed computing environments / large-scale speech applications / block coordinate descent algorithm / belief networks / parallel processing / computing / convex network / out-of-core solution / /

MarketIndex

SET 50 / /

Organization

Defense Advanced Research Projects Agency / University of Illinois / Department of Electrical and Computer Engineering / MIT / Ensemble of Kernel Machines As / /

Person

J. Yang / V / B. Kings / P. Nguyen / Tara N. Sainath / V. Vanhoucke / Cho / Haim Avron / T. Sainath / Saul / /

Position

Wb / /

Product

C-0323 / /

ProvinceOrState

Illinois / /

PublishedMedium

Machine Learning / Journal of Machine Learning Research / /

RadioStation

DNN / /

Technology

speech recognition / X. All processors / block coordinate descent algorithm / neural network / optimization algorithms / proposed algorithms / Machine Learning / th Algorithm / parallel processing / /

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