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Sun Nov 19 06:45:15 2017

Information about: hunch.net
Popularity/access rank: Site number 33463 (.net extension); 585657 (global rank)
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TitleMachine Learning (Theory) (view sites with similar title)
Category: Computers / Artificial Intelligence / Machine Learning / Software
Description Machine Learning (Theory) 10/26/2012 ML Symposium and Strata/Hadoop World Tags: Conferences , Workshop — jl @ 11:40 am The New York ML symposium was last Friday. There were 303 registrations, up a bit from last year . I particularly enjoyed talks by...
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Hunch - Site Review: Machine Learning (Theory) - A collaborative machine learning weblog by John Langford.


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Hunch Summary

Machine Learning (Theory) Machine Learning (Theory) 10/26/2012. ML Symposium and Strata/Hadoop World.
Tags: Conferences , Workshop — jl @ 11:40 am.The New York ML symposium was last Friday. There were 303 registrations, up a bit from last year . I particularly enjoyed talks by Bill Freeman on vision and ML, Jon Lenchner on strategy in Jeopardy, and Tara N. Sainath and Brian Kingsbury on deep learning for speech recognition . If anyone has suggestions or thoughts for next year, please speak up.
I also attended Strata + Hadoop World for the first time. This is primarily a trade conference rather than an academic conference, but I found it pretty interesting as a first time attendee. This is ground zero for the Big data buzzword, and I see now why. It’s about data, and the word “big” is so ambiguous that everyone can lay claim to it. There were essentially zero academic talks. Instead, the focus was on war stories, product announcements, and education. The general level of education is much ...



Popularity Rank of hunch.net
Alexa Rank Alexa Rank Date
585657 2013-05-15
791074 2013-04-15
863178 2013-04-01
788227 2013-01-08
672919 2012-11-17
579730 2011-12-06
609629 2010-06-10
744169 2009-05-28

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Server IP of hunch.net: 69.64.47.173 (hosted by Hosting Solutions International)
Domain extension: .net (list top sites in .net (Networks))

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  1. Machine Learning (Theory)
  2. ML Symposium and Strata/Hadoop World
  3. Conferences
  4. Workshop
  5. jl
  6. New York ML symposium
  7. last year
  8. Bill Freeman
  9. Jon Lenchner
  10. Tara N. Sainath
  11. deep learning for speech recognition
  12. Strata + Hadoop World
  13. Big data
  14. Github
  15. Zillow
  16. Pandas
  17. Ted Dunning
  18. streaming k-means algorithm
  19. 7th Annual Machine Learning Symposium
  20. Announcements
  21. Machine Learning
  22. jake
  23. New York Academy of Sciences
  24. 7th Annual Machine Learning Symposium
  25. Peter Bartlett
  26. Vladimir Vapnik
  27. hackNY
  28. Microsoft Research
  29. bit.ly
  30. Buzzfeed
  31. Chartbeat
  32. Sense Networks
  33. Visual Revenue
  34. Vowpal Wabbit, version 7.0
  35. Code
  36. Exploration
  37. Reductions
  38. VW
  39. out
  40. learning reductions
  41. Microsoft
  42. Stephane
  43. NYAS ML 2012 and ICML 2013
  44. ICML 2013
  45. Patterns for research in machine learning
  46. Teaching
  47. smalieslami
  48. a handful of basic code patterns
  49. Charles Sutton’s
  50. HackerNews’
  51. MUCMD and BayLearn
  52. Meaningful Use of Complex Medical Data
  53. UAI
  54. Bay Area Machine Learning Symposium
  55. Google
  56. Videolectures
  57. Yaser
  58. videotaped machine learning lectures
  59. Caltech
  60. Andrew Ng
  61. ML class
  62. ICML survey and comments
  63. Reviewing
  64. ICML
  65. Mark Reid
  66. ICML discussion
  67. survey results
  68. ICML 2010 survey results
  69. Normal Deviate and the UCSC Machine Learning Summer School
  70. Larry Wasserman
  71. Normal Deviate
  72. Manfred Warmuth
  73. UCSC machine learning summer school
  74. ICML acceptance statistics
  75. Brendan’s graph
  76. spof
  77. ICML accepted papers and early registration
  78. accepted papers
  79. early registration
  80. Microsoft Research, New York City
  81. Research
  82. Yahoo! laid off people
  83. Yahoo! Research
  84. Prabhakar
  85. simple act of leaving
  86. Rob
  87. KDD
  88. news story
  89. Microsoft Research New York City
  90. Joelle
  91. Deepak
  92. Linkedin
  93. ultrascale learning algorithm
  94. crosspost on CACM
  95. Lance
  96. Jennifer
  97. NYTimes
  98. Vader
  99. ICML: Behind the Scenes
  100. Mahdi
  101. CMT
  102. TMS
  103. Laurent Charlin
  104. Rich Zemel
  105. Vowpal Wabbit
  106. here
  107. double submission guide
  108. Both new: STOC workshops and NEML
  109. Theory
  110. New England Machine Learning Day
  111. STOC
  112. workshops
  113. Algorithms for Distributed and Streaming Data
  114. ICML author feedback is open



site visit date: 2012-12-11 22:44:55
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