hunch_net hunch_net-2007 hunch_net-2007-273 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: … is in Kioloa, Australia from March 3 to March 14. It’s a great chance to learn something about Machine Learning and I’ve enjoyed several previous Machine Learning Summer Schools . The website has many more details , but registration is open now for the first 80 to sign up.
sentIndex sentText sentNum sentScore
1 It’s a great chance to learn something about Machine Learning and I’ve enjoyed several previous Machine Learning Summer Schools . [sent-2, score-1.05]
2 The website has many more details , but registration is open now for the first 80 to sign up. [sent-3, score-1.275]
wordName wordTfidf (topN-words)
[('march', 0.498), ('australia', 0.356), ('schools', 0.315), ('sign', 0.281), ('registration', 0.254), ('website', 0.241), ('enjoyed', 0.227), ('summer', 0.218), ('chance', 0.19), ('details', 0.179), ('previous', 0.172), ('open', 0.172), ('learn', 0.134), ('something', 0.125), ('great', 0.124), ('ve', 0.123), ('machine', 0.118), ('first', 0.099), ('several', 0.078), ('many', 0.049), ('learning', 0.048)]
simIndex simValue blogId blogTitle
same-blog 1 1.0 273 hunch net-2007-11-16-MLSS 2008
Introduction: … is in Kioloa, Australia from March 3 to March 14. It’s a great chance to learn something about Machine Learning and I’ve enjoyed several previous Machine Learning Summer Schools . The website has many more details , but registration is open now for the first 80 to sign up.
2 0.29858342 11 hunch net-2005-02-02-Paper Deadlines
Introduction: It’s conference season, and smell of budding papers is in the air. IJCAI 2005 , January 21 COLT 2005 , February 2 KDD 2005 , February 18 ICML 2005 , March 8 UAI 2005 , March 16 AAAI 2005 , March 18
3 0.21293733 130 hunch net-2005-11-16-MLSS 2006
Introduction: There will be two machine learning summer schools in 2006. One is in Canberra, Australia from February 6 to February 17 (Aussie summer). The webpage is fully ‘live’ so you should actively consider it now. The other is in Taipei, Taiwan from July 24 to August 4. This one is still in the planning phase, but that should be settled soon. Attending an MLSS is probably the quickest and easiest way to bootstrap yourself into a reasonable initial understanding of the field of machine learning.
4 0.16943486 292 hunch net-2008-03-15-COLT Open Problems
Introduction: COLT has a call for open problems due March 21. I encourage anyone with a specifiable open problem to write it down and send it in. Just the effort of specifying an open problem precisely and concisely has been very helpful for my own solutions, and there is a substantial chance others will solve it. To increase the chance someone will take it up, you can even put a bounty on the solution. (Perhaps I should raise the $500 bounty on the K-fold cross-validation problem as it hasn’t yet been solved).
5 0.14852531 494 hunch net-2014-03-11-The New York ML Symposium, take 2
Introduction: The 20 13 14 is New York Machine Learning Symposium is finally happening on March 28th at the New York Academy of Science . Every invited speaker interests me personally. They are: Rayid Ghani (Chief Scientist at Obama 2012) Brian Kingsbury (Speech Recognition @ IBM) Jorge Nocedal (who did LBFGS) We’ve been somewhat disorganized in advertising this. As a consequence, anyone who has not submitted an abstract but would like to do so may send one directly to me (jl@hunch.net title NYASMLS) by Friday March 14. I will forward them to the rest of the committee for consideration.
6 0.14195289 425 hunch net-2011-02-25-Yahoo! Machine Learning grant due March 11
7 0.14025059 4 hunch net-2005-01-26-Summer Schools
8 0.1298048 482 hunch net-2013-05-04-COLT and ICML registration
9 0.12064557 145 hunch net-2005-12-29-Deadline Season
10 0.10724886 198 hunch net-2006-07-25-Upcoming conference
11 0.097821161 242 hunch net-2007-04-30-COLT 2007
12 0.097593054 458 hunch net-2012-03-06-COLT-ICML Open Questions and ICML Instructions
13 0.097524151 375 hunch net-2009-10-26-NIPS workshops
14 0.096512996 357 hunch net-2009-05-30-Many ways to Learn this summer
15 0.090914845 457 hunch net-2012-02-29-Key Scientific Challenges and the Franklin Symposium
16 0.088335916 146 hunch net-2006-01-06-MLTV
17 0.087753788 297 hunch net-2008-04-22-Taking the next step
18 0.08735083 422 hunch net-2011-01-16-2011 Summer Conference Deadline Season
19 0.086666532 465 hunch net-2012-05-12-ICML accepted papers and early registration
20 0.083217286 389 hunch net-2010-02-26-Yahoo! ML events
topicId topicWeight
[(0, 0.107), (1, -0.109), (2, -0.122), (3, -0.125), (4, -0.061), (5, -0.138), (6, -0.035), (7, 0.01), (8, -0.07), (9, -0.057), (10, 0.006), (11, -0.048), (12, 0.053), (13, 0.046), (14, 0.016), (15, 0.024), (16, -0.057), (17, 0.05), (18, -0.033), (19, 0.042), (20, -0.0), (21, 0.175), (22, 0.02), (23, -0.238), (24, -0.009), (25, 0.045), (26, 0.021), (27, -0.036), (28, 0.172), (29, -0.017), (30, 0.039), (31, 0.087), (32, 0.117), (33, -0.134), (34, -0.028), (35, -0.153), (36, 0.006), (37, -0.096), (38, 0.079), (39, -0.063), (40, 0.087), (41, -0.016), (42, -0.068), (43, 0.127), (44, -0.006), (45, -0.034), (46, -0.025), (47, 0.033), (48, -0.039), (49, -0.123)]
simIndex simValue blogId blogTitle
same-blog 1 0.94802427 273 hunch net-2007-11-16-MLSS 2008
Introduction: … is in Kioloa, Australia from March 3 to March 14. It’s a great chance to learn something about Machine Learning and I’ve enjoyed several previous Machine Learning Summer Schools . The website has many more details , but registration is open now for the first 80 to sign up.
2 0.66627127 130 hunch net-2005-11-16-MLSS 2006
Introduction: There will be two machine learning summer schools in 2006. One is in Canberra, Australia from February 6 to February 17 (Aussie summer). The webpage is fully ‘live’ so you should actively consider it now. The other is in Taipei, Taiwan from July 24 to August 4. This one is still in the planning phase, but that should be settled soon. Attending an MLSS is probably the quickest and easiest way to bootstrap yourself into a reasonable initial understanding of the field of machine learning.
3 0.56627733 292 hunch net-2008-03-15-COLT Open Problems
Introduction: COLT has a call for open problems due March 21. I encourage anyone with a specifiable open problem to write it down and send it in. Just the effort of specifying an open problem precisely and concisely has been very helpful for my own solutions, and there is a substantial chance others will solve it. To increase the chance someone will take it up, you can even put a bounty on the solution. (Perhaps I should raise the $500 bounty on the K-fold cross-validation problem as it hasn’t yet been solved).
4 0.53248489 11 hunch net-2005-02-02-Paper Deadlines
Introduction: It’s conference season, and smell of budding papers is in the air. IJCAI 2005 , January 21 COLT 2005 , February 2 KDD 2005 , February 18 ICML 2005 , March 8 UAI 2005 , March 16 AAAI 2005 , March 18
5 0.46349317 459 hunch net-2012-03-13-The Submodularity workshop and Lucca Professorship
Introduction: Nina points out the Submodularity Workshop March 19-20 next week at Georgia Tech . Many people want to make Submodularity the new Convexity in machine learning, and it certainly seems worth exploring. Sara Olson also points out a tenured faculty position at IMT Lucca with a deadline of May 15th . Lucca happens to be the ancestral home of 1/4 of my heritage
6 0.45743385 458 hunch net-2012-03-06-COLT-ICML Open Questions and ICML Instructions
7 0.44967973 203 hunch net-2006-08-18-Report of MLSS 2006 Taipei
8 0.44292393 429 hunch net-2011-04-06-COLT open questions
9 0.44093341 494 hunch net-2014-03-11-The New York ML Symposium, take 2
10 0.41971782 4 hunch net-2005-01-26-Summer Schools
11 0.40922055 105 hunch net-2005-08-23-(Dis)similarities between academia and open source programmers
12 0.40272084 425 hunch net-2011-02-25-Yahoo! Machine Learning grant due March 11
13 0.39861277 457 hunch net-2012-02-29-Key Scientific Challenges and the Franklin Symposium
14 0.39027247 75 hunch net-2005-05-28-Running A Machine Learning Summer School
15 0.3897554 357 hunch net-2009-05-30-Many ways to Learn this summer
16 0.3844268 465 hunch net-2012-05-12-ICML accepted papers and early registration
17 0.3593027 145 hunch net-2005-12-29-Deadline Season
18 0.35800683 422 hunch net-2011-01-16-2011 Summer Conference Deadline Season
19 0.35742137 389 hunch net-2010-02-26-Yahoo! ML events
20 0.3503938 297 hunch net-2008-04-22-Taking the next step
topicId topicWeight
[(27, 0.143), (38, 0.067), (55, 0.162), (86, 0.437)]
simIndex simValue blogId blogTitle
1 0.89115369 54 hunch net-2005-04-08-Fast SVMs
Introduction: There was a presentation at snowbird about parallelized support vector machines. In many cases, people parallelize by ignoring serial operations, but that is not what happened here—they parallelize with optimizations. Consequently, this seems to be the fastest SVM in existence. There is a related paper here .
2 0.81131113 477 hunch net-2013-01-01-Deep Learning 2012
Introduction: 2012 was a tumultuous year for me, but it was undeniably a great year for deep learning efforts. Signs of this include: Winning a Kaggle competition . Wide adoption of deep learning for speech recognition . Significant industry support . Gains in image recognition . This is a rare event in research: a significant capability breakout. Congratulations are definitely in order for those who managed to achieve it. At this point, deep learning algorithms seem like a choice undeniably worth investigating for real applications with significant data.
same-blog 3 0.79663587 273 hunch net-2007-11-16-MLSS 2008
Introduction: … is in Kioloa, Australia from March 3 to March 14. It’s a great chance to learn something about Machine Learning and I’ve enjoyed several previous Machine Learning Summer Schools . The website has many more details , but registration is open now for the first 80 to sign up.
4 0.55482435 281 hunch net-2007-12-21-Vowpal Wabbit Code Release
Introduction: We are releasing the Vowpal Wabbit (Fast Online Learning) code as open source under a BSD (revised) license. This is a project at Yahoo! Research to build a useful large scale learning algorithm which Lihong Li , Alex Strehl , and I have been working on. To appreciate the meaning of “large”, it’s useful to define “small” and “medium”. A “small” supervised learning problem is one where a human could use a labeled dataset and come up with a reasonable predictor. A “medium” supervised learning problem dataset fits into the RAM of a modern desktop computer. A “large” supervised learning problem is one which does not fit into the RAM of a normal machine. VW tackles large scale learning problems by this definition of large. I’m not aware of any other open source Machine Learning tools which can handle this scale (although they may exist). A few close ones are: IBM’s Parallel Machine Learning Toolbox isn’t quite open source . The approach used by this toolbox is essenti
5 0.38556683 270 hunch net-2007-11-02-The Machine Learning Award goes to …
Introduction: Perhaps the biggest CS prize for research is the Turing Award , which has a $0.25M cash prize associated with it. It appears none of the prizes so far have been for anything like machine learning (the closest are perhaps database awards). In CS theory, there is the Gödel Prize which is smaller and newer, offering a $5K prize along and perhaps (more importantly) recognition. One such award has been given for Machine Learning, to Robert Schapire and Yoav Freund for Adaboost. In Machine Learning, there seems to be no equivalent of these sorts of prizes. There are several plausible reasons for this: There is no coherent community. People drift in and out of the central conferences all the time. Most of the author names from 10 years ago do not occur in the conferences of today. In addition, the entire subject area is fairly new. There are at least a core group of people who have stayed around. Machine Learning work doesn’t last Almost every paper is fo
6 0.38142079 395 hunch net-2010-04-26-Compassionate Reviewing
7 0.37538919 90 hunch net-2005-07-07-The Limits of Learning Theory
8 0.37536049 453 hunch net-2012-01-28-Why COLT?
9 0.37106162 452 hunch net-2012-01-04-Why ICML? and the summer conferences
10 0.36538017 20 hunch net-2005-02-15-ESPgame and image labeling
11 0.36459637 448 hunch net-2011-10-24-2011 ML symposium and the bears
12 0.36457738 302 hunch net-2008-05-25-Inappropriate Mathematics for Machine Learning
13 0.35814151 40 hunch net-2005-03-13-Avoiding Bad Reviewing
14 0.35803407 446 hunch net-2011-10-03-Monday announcements
15 0.35743254 271 hunch net-2007-11-05-CMU wins DARPA Urban Challenge
16 0.35700899 484 hunch net-2013-06-16-Representative Reviewing
17 0.35669512 65 hunch net-2005-05-02-Reviewing techniques for conferences
18 0.35471377 89 hunch net-2005-07-04-The Health of COLT
19 0.35396671 116 hunch net-2005-09-30-Research in conferences
20 0.34993976 443 hunch net-2011-09-03-Fall Machine Learning Events