“Readings”版本间的差异

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Machine Learning
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=Speech recognition=
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[[Public Research Tools]]
 
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1. Noise robustness
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http://www1.icsi.berkeley.edu/Speech/papers/gelbart-ms/pointers/
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2. Qualcomm-ICSI-OGI front end
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http://www1.icsi.berkeley.edu/Speech/papers/qio/
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=Machine Learning=
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1. general Bayesian inference: doc and tool
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http://ksvanhorn.com/bayes/free-bayes-software.html
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2. The Variantional Bayesian toolkit:
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http://www.gatsby.ucl.ac.uk/vbayes/vbsoftware.html
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The sampling based Bayesian approach can be obtained here:
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http://www.mrc-bsu.cam.ac.uk/bugs/
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3. Topic models from David Blei
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http://www.cs.princeton.edu/~blei/topicmodeling.html
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4. MCMC approaches also here
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http://www.mppmu.mpg.de/bat/
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http://www.mpe.mpg.de/~aws/BayesForum_201204_KK.pdf
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5. Topic models Biography
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http://www.cs.princeton.edu/~mimno/topics.html
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6. Gibbs LDA
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http://gibbslda.sourceforge.net/
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7. Tools for DPMM
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Jacobei
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https://github.com/jacobeisenstein/DPMM
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Sickit
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http://scikit-learn.org/0.11/index.html
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Hains
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http://code.google.com/p/haines/
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8. Sparse SVMs
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http://www.enm.bris.ac.uk/staff/xkh/
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9. Lasso
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http://www-stat.stanford.edu/~tibs/lasso.html
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10. Sparse LU
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http://crd-legacy.lbl.gov/~xiaoye/SuperLU/
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11. Ensemble SVM
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http://homes.esat.kuleuven.be/~claesenm/ensemblesvm/
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12. Online learning toolkit
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http://www.cais.ntu.edu.sg/~chhoi/libol/
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13. VBEM-GMM
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http://www.cs.ubc.ca/~murphyk/Software/VBEMGMM/index.html
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14. pylearn2
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http://deeplearning.net/software/pylearn2/
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=NLP toolkits and resources=
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1. Stanford tools
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http://nlp.stanford.edu/software/
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2. Traditional Chinese public dictionary and statistics
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http://www.edu.tw/files/site_content/m0001/pin/yu7.htm?open
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3. Idiom Traditional Chinese public words
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http://dict.idioms.moe.edu.tw/cydic/index.htm
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4. RNN toolkit from microsoft
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http://research.microsoft.com/en-us/projects/rnn/
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5. A bunch of resources
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http://www-nlp.stanford.edu/links/statnlp.html
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=SID toolits=
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1. Alize from Avignon
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http://mistral.univ-avignon.fr/download_en.html
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2. Idiap
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https://github.com/bioidiap/xbob.spkrec
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http://www.idiap.ch/~marcel/professional/Resources.html
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=People you may want to follow=
 
=People you may want to follow=
  

2014年9月23日 (二) 00:32的版本

Public Research Tools

People you may want to follow

1. Dong Yu, MS

http://research.microsoft.com/en-us/people/dongyu/

2. Geoffrey E. Hinton, U. Toronto

http://www.cs.toronto.edu/~hinton/

3. Deng Li, MS

http://research.microsoft.com/en-us/people/deng/

4. Tara Sainath, IBM

http://www.researchgate.net/profile/Tara_Sainath/publications/

http://www.informatik.uni-trier.de/~ley/pers/hd/s/Sainath:Tara_N=.html

5. Daniel Povey, JHU

http://www.danielpovey.com/

6. Patrick Kenny, CRIM

http://www.crim.ca/perso/patrick.kenny/

7. Nelson Morgan, ICSI Berkley

http://www1.icsi.berkeley.edu/~morgan/