“Readings”版本间的差异

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Machine Learning
 
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=Speech recognition=
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[[New member reading list]]
  
1. Noise robustness
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[[Advanced member reading list]]
  
http://www1.icsi.berkeley.edu/Speech/papers/gelbart-ms/pointers/
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[[Public Research Tools]]
  
2. Qualcomm-ICSI-OGI front end
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[[People you may want to follow]]
  
http://www1.icsi.berkeley.edu/Speech/papers/qio/
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[[Other teams]]
  
=Machine Learning=
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[[Journals you may submit to]]
  
1. general Bayesian inference: doc and tool
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[[Free libraries]]
  
http://ksvanhorn.com/bayes/free-bayes-software.html
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[[Data resources]]
  
2. The Variantional Bayesian toolkit:
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[[Seminars]]
  
http://www.gatsby.ucl.ac.uk/vbayes/vbsoftware.html
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[[Machine Learning Book]]
  
The sampling based Bayesian approach can be obtained here:
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[[AI Book]]
 
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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=
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1. Dong Yu, MS
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http://research.microsoft.com/en-us/people/dongyu/
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2. Geoffrey E. Hinton, U. Toronto
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http://www.cs.toronto.edu/~hinton/
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3. Deng Li, MS
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http://research.microsoft.com/en-us/people/deng/
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4. Tara Sainath, IBM
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http://www.researchgate.net/profile/Tara_Sainath/publications/
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http://www.informatik.uni-trier.de/~ley/pers/hd/s/Sainath:Tara_N=.html
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5. Daniel Povey, JHU
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http://www.danielpovey.com/
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6. Patrick Kenny, CRIM
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http://www.crim.ca/perso/patrick.kenny/
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7. Nelson Morgan, ICSI Berkley
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http://www1.icsi.berkeley.edu/~morgan/
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2018年8月23日 (四) 13:50的最后版本

New member reading list

Advanced member reading list

Public Research Tools

People you may want to follow

Other teams

Journals you may submit to

Free libraries

Data resources

Seminars

Machine Learning Book

AI Book