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Aurélien Bellet

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with Avner May

> Home > Persons > Aurélien Bellet

Publications

2019
[j8]
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  dblp key:
  - journals/jmlr/MayGLGLBFCHKPS19
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  - https://dblp.org/rec/journals/jmlr/MayGLGLBFCHKPS19
Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel Hsu, Brian Kingsbury, Michael Picheny, Fei Sha:
Kernel Approximation Methods for Speech Recognition. J. Mach. Learn. Res. 20: 59:1-59:36 (2019)
2017
[i15]
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  - journals/corr/MayGLGLBFCHKPS17
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  - https://dblp.org/rec/journals/corr/MayGLGLBFCHKPS17
Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel J. Hsu, Brian Kingsbury, Michael Picheny, Fei Sha:
Kernel Approximation Methods for Speech Recognition. CoRR abs/1701.03577 (2017)
2016
[c12]
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  dblp key:
  - conf/icassp/LuQGLMBFCKPS16
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  - https://dblp.org/rec/conf/icassp/LuQGLMBFCKPS16
Zhiyun Lu, Dong Guo, Alireza Bagheri Garakani, Kuan Liu, Avner May, Aurélien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, Fei Sha:
A comparison between deep neural nets and kernel acoustic models for speech recognition. ICASSP 2016: 5070-5074
[i12]
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  - journals/corr/LuGGLMBFCKPS16
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  - https://dblp.org/rec/journals/corr/LuGGLMBFCKPS16
Zhiyun Lu, Dong Guo, Alireza Bagheri Garakani, Kuan Liu, Avner May, Aurélien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, Fei Sha:
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition. CoRR abs/1603.05800 (2016)
2014
[i4]
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  dblp key:
  - journals/corr/LuMLGGBFCKPS14
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Zhiyun Lu, Avner May, Kuan Liu, Alireza Bagheri Garakani, Dong Guo, Aurélien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, Fei Sha:
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets. CoRR abs/1411.4000 (2014)

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