“Readings”版本间的差异

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=Machine Learning=
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[[New member reading list]]
  
1. general Bayesian inference: doc and tool
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[[Advanced member reading list]]
  
http://ksvanhorn.com/bayes/free-bayes-software.html
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[[Public Research Tools]]
  
2. The Variantional Bayesian toolkit:
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[[People you may want to follow]]
  
http://www.gatsby.ucl.ac.uk/vbayes/vbsoftware.html
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[[Other teams]]
  
The sampling based Bayesian approach can be obtained here:
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[[Journals you may submit to]]
  
http://www.mrc-bsu.cam.ac.uk/bugs/
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[[Free libraries]]
  
3. Topic models from David Blei
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[[Data resources]]
  
http://www.cs.princeton.edu/~blei/topicmodeling.html
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[[Seminars]]
  
4. MCMC approaches also here
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[[Machine Learning Book]]
  
http://www.mppmu.mpg.de/bat/
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[[AI Book]]
 
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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. Variational Bayesian
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http://www.variational-bayes.org/vbsoftware.html
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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