hunch_net hunch_net-2007 hunch_net-2007-271 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: The results have been posted , with CMU first , Stanford second , and Virginia Tech Third . Considering that this was an open event (at least for people in the US), this was a very strong showing for research at universities (instead of defense contractors, for example). Some details should become public at the NIPS workshops . Slashdot has a post with many comments.
sentIndex sentText sentNum sentScore
1 The results have been posted , with CMU first , Stanford second , and Virginia Tech Third . [sent-1, score-0.584]
2 Considering that this was an open event (at least for people in the US), this was a very strong showing for research at universities (instead of defense contractors, for example). [sent-2, score-1.477]
3 Some details should become public at the NIPS workshops . [sent-3, score-0.622]
wordName wordTfidf (topN-words)
[('defense', 0.335), ('stanford', 0.31), ('tech', 0.31), ('slashdot', 0.268), ('posted', 0.258), ('universities', 0.237), ('cmu', 0.237), ('third', 0.208), ('showing', 0.205), ('event', 0.192), ('considering', 0.181), ('comments', 0.174), ('public', 0.17), ('workshops', 0.166), ('details', 0.147), ('open', 0.142), ('become', 0.139), ('nips', 0.136), ('strong', 0.132), ('second', 0.131), ('instead', 0.131), ('post', 0.124), ('results', 0.114), ('us', 0.109), ('least', 0.108), ('first', 0.081), ('research', 0.074), ('example', 0.064), ('people', 0.052), ('many', 0.041)]
simIndex simValue blogId blogTitle
same-blog 1 1.0 271 hunch net-2007-11-05-CMU wins DARPA Urban Challenge
Introduction: The results have been posted , with CMU first , Stanford second , and Virginia Tech Third . Considering that this was an open event (at least for people in the US), this was a very strong showing for research at universities (instead of defense contractors, for example). Some details should become public at the NIPS workshops . Slashdot has a post with many comments.
2 0.24087399 119 hunch net-2005-10-08-We have a winner
Introduction: The DARPA grandchallenge is a big contest for autonomous robot vehicle driving. It was run once in 2004 for the first time and all teams did badly. This year was notably different with the Stanford and CMU teams succesfully completing the course. A number of details are here and wikipedia has continuing coverage . A formal winner hasn’t been declared yet although Stanford completed the course quickest. The Stanford and CMU teams deserve a large round of applause as they have strongly demonstrated the feasibility of autonomous vehicles. The good news for machine learning is that the Stanford team (at least) is using some machine learning techniques.
3 0.13663638 285 hunch net-2008-01-23-Why Workshop?
Introduction: I second the call for workshops at ICML/COLT/UAI . Several times before , details of why and how to run a workshop have been mentioned. There is a simple reason to prefer workshops here: attendance. The Helsinki colocation has placed workshops directly between ICML and COLT/UAI , which is optimal for getting attendees from any conference. In addition, last year ICML had relatively few workshops and NIPS workshops were overloaded. In addition to those that happened a similar number were rejected. The overload has strange consequences—for example, the best attended workshop wasn’t an official NIPS workshop. Aside from intrinsic interest, the Deep Learning workshop benefited greatly from being off schedule.
4 0.12465922 59 hunch net-2005-04-22-New Blog: [Lowerbounds,Upperbounds]
Introduction: Maverick Woo and the Aladdin group at CMU have started a CS theory-related blog here .
5 0.12463093 445 hunch net-2011-09-28-Somebody’s Eating Your Lunch
Introduction: Since we last discussed the other online learning , Stanford has very visibly started pushing mass teaching in AI , Machine Learning , and Databases . In retrospect, it’s not too surprising that the next step up in serious online teaching experiments are occurring at the computer science department of a university embedded in the land of startups. Numbers on the order of 100000 are quite significant—similar in scale to the number of computer science undergraduate students/year in the US. Although these populations surely differ, the fact that they could overlap is worth considering for the future. It’s too soon to say how successful these classes will be and there are many easy criticisms to make: Registration != Learning … but if only 1/10th complete these classes, the scale of teaching still surpasses the scale of any traditional process. 1st year excitement != nth year routine … but if only 1/10th take future classes, the scale of teaching still surpass
6 0.11781882 344 hunch net-2009-02-22-Effective Research Funding
7 0.10953346 372 hunch net-2009-09-29-Machine Learning Protests at the G20
8 0.10891369 216 hunch net-2006-11-02-2006 NIPS workshops
9 0.10654576 379 hunch net-2009-11-23-ICML 2009 Workshops (and Tutorials)
10 0.10436411 418 hunch net-2010-12-02-Traffic Prediction Problem
11 0.10431749 29 hunch net-2005-02-25-Solution: Reinforcement Learning with Classification
12 0.10323177 399 hunch net-2010-05-20-Google Predict
13 0.090962633 50 hunch net-2005-04-01-Basic computer science research takes a hit
14 0.090637788 71 hunch net-2005-05-14-NIPS
15 0.087696902 46 hunch net-2005-03-24-The Role of Workshops
16 0.083290868 113 hunch net-2005-09-19-NIPS Workshops
17 0.081149131 122 hunch net-2005-10-13-Site tweak
18 0.079113126 297 hunch net-2008-04-22-Taking the next step
19 0.078494266 375 hunch net-2009-10-26-NIPS workshops
20 0.076975025 141 hunch net-2005-12-17-Workshops as Franchise Conferences
topicId topicWeight
[(0, 0.116), (1, -0.081), (2, -0.108), (3, -0.007), (4, -0.043), (5, 0.093), (6, 0.103), (7, -0.002), (8, 0.036), (9, 0.077), (10, -0.029), (11, 0.079), (12, -0.07), (13, -0.077), (14, -0.034), (15, 0.011), (16, -0.069), (17, -0.047), (18, 0.024), (19, 0.077), (20, -0.104), (21, 0.058), (22, -0.027), (23, -0.057), (24, -0.016), (25, -0.069), (26, -0.02), (27, 0.041), (28, 0.085), (29, 0.021), (30, 0.095), (31, 0.01), (32, -0.035), (33, 0.056), (34, -0.068), (35, 0.013), (36, 0.053), (37, -0.119), (38, 0.109), (39, 0.012), (40, 0.011), (41, 0.026), (42, 0.075), (43, 0.063), (44, 0.085), (45, -0.13), (46, 0.103), (47, 0.152), (48, 0.087), (49, 0.016)]
simIndex simValue blogId blogTitle
same-blog 1 0.97747552 271 hunch net-2007-11-05-CMU wins DARPA Urban Challenge
Introduction: The results have been posted , with CMU first , Stanford second , and Virginia Tech Third . Considering that this was an open event (at least for people in the US), this was a very strong showing for research at universities (instead of defense contractors, for example). Some details should become public at the NIPS workshops . Slashdot has a post with many comments.
2 0.68761957 119 hunch net-2005-10-08-We have a winner
Introduction: The DARPA grandchallenge is a big contest for autonomous robot vehicle driving. It was run once in 2004 for the first time and all teams did badly. This year was notably different with the Stanford and CMU teams succesfully completing the course. A number of details are here and wikipedia has continuing coverage . A formal winner hasn’t been declared yet although Stanford completed the course quickest. The Stanford and CMU teams deserve a large round of applause as they have strongly demonstrated the feasibility of autonomous vehicles. The good news for machine learning is that the Stanford team (at least) is using some machine learning techniques.
3 0.51046157 105 hunch net-2005-08-23-(Dis)similarities between academia and open source programmers
Introduction: Martin Pool and I recently discussed the similarities and differences between academia and open source programming. Similarities: Cost profile Research and programming share approximately the same cost profile: A large upfront effort is required to produce something useful, and then “anyone” can use it. (The “anyone” is not quite right for either group because only sufficiently technical people could use it.) Wealth profile A “wealthy” academic or open source programmer is someone who has contributed a lot to other people in research or programs. Much of academia is a “gift culture”: whoever gives the most is most respected. Problems Both academia and open source programming suffer from similar problems. Whether or not (and which) open source program is used are perhaps too-often personality driven rather than driven by capability or usefulness. Similar phenomena can happen in academia with respect to directions of research. Funding is often a problem for
4 0.49942175 285 hunch net-2008-01-23-Why Workshop?
Introduction: I second the call for workshops at ICML/COLT/UAI . Several times before , details of why and how to run a workshop have been mentioned. There is a simple reason to prefer workshops here: attendance. The Helsinki colocation has placed workshops directly between ICML and COLT/UAI , which is optimal for getting attendees from any conference. In addition, last year ICML had relatively few workshops and NIPS workshops were overloaded. In addition to those that happened a similar number were rejected. The overload has strange consequences—for example, the best attended workshop wasn’t an official NIPS workshop. Aside from intrinsic interest, the Deep Learning workshop benefited greatly from being off schedule.
5 0.48241973 29 hunch net-2005-02-25-Solution: Reinforcement Learning with Classification
Introduction: I realized that the tools needed to solve the problem just posted were just created. I tried to sketch out the solution here (also in .lyx and .tex ). It is still quite sketchy (and probably only the few people who understand reductions well can follow). One of the reasons why I started this weblog was to experiment with “research in the open”, and this is an opportunity to do so. Over the next few days, I’ll be filling in details and trying to get things to make sense. If you have additions or ideas, please propose them.
6 0.47816396 266 hunch net-2007-10-15-NIPS workshops extended to 3 days
7 0.476538 297 hunch net-2008-04-22-Taking the next step
8 0.46855348 216 hunch net-2006-11-02-2006 NIPS workshops
9 0.46610069 71 hunch net-2005-05-14-NIPS
10 0.4657107 372 hunch net-2009-09-29-Machine Learning Protests at the G20
11 0.43863732 379 hunch net-2009-11-23-ICML 2009 Workshops (and Tutorials)
12 0.43760905 367 hunch net-2009-08-16-Centmail comments
13 0.40420642 264 hunch net-2007-09-30-NIPS workshops are out.
14 0.39654353 296 hunch net-2008-04-21-The Science 2.0 article
15 0.37808558 46 hunch net-2005-03-24-The Role of Workshops
16 0.35923687 424 hunch net-2011-02-17-What does Watson mean?
17 0.35427296 59 hunch net-2005-04-22-New Blog: [Lowerbounds,Upperbounds]
18 0.34918883 399 hunch net-2010-05-20-Google Predict
19 0.34524593 333 hunch net-2008-12-27-Adversarial Academia
20 0.33506966 488 hunch net-2013-08-31-Extreme Classification workshop at NIPS
topicId topicWeight
[(27, 0.122), (55, 0.714)]
simIndex simValue blogId blogTitle
1 0.9972958 472 hunch net-2012-08-27-NYAS ML 2012 and ICML 2013
Introduction: The New York Machine Learning Symposium is October 19 with a 2 page abstract deadline due September 13 via email with subject “Machine Learning Poster Submission” sent to physicalscience@nyas.org. Everyone is welcome to submit. Last year’s attendance was 246 and I expect more this year. The primary experiment for ICML 2013 is multiple paper submission deadlines with rolling review cycles. The key dates are October 1, December 15, and February 15. This is an attempt to shift ICML further towards a journal style review process and reduce peak load. The “not for proceedings” experiment from this year’s ICML is not continuing. Edit: Fixed second ICML deadline.
same-blog 2 0.9928121 271 hunch net-2007-11-05-CMU wins DARPA Urban Challenge
Introduction: The results have been posted , with CMU first , Stanford second , and Virginia Tech Third . Considering that this was an open event (at least for people in the US), this was a very strong showing for research at universities (instead of defense contractors, for example). Some details should become public at the NIPS workshops . Slashdot has a post with many comments.
3 0.99272448 446 hunch net-2011-10-03-Monday announcements
Introduction: Various people want to use hunch.net to announce things. I’ve generally resisted this because I feared hunch becoming a pure announcement zone while I am much more interested contentful posts and discussion personally. Nevertheless there is clearly some value and announcements are easy, so I’m planning to summarize announcements on Mondays. D. Sculley points out an interesting Semisupervised feature learning competition, with a deadline of October 17. Lihong Li points out the webscope user interaction dataset which is the first high quality exploration dataset I’m aware of that is publicly available. Seth Rogers points out CrossValidated which looks similar in conception to metaoptimize , but directly using the stackoverflow interface and with a bit more of a statistics twist.
4 0.98581195 326 hunch net-2008-11-11-COLT CFP
Introduction: Adam Klivans , points out the COLT call for papers . The important points are: Due Feb 13. Montreal, June 18-21. This year, there is author feedback.
5 0.98581195 465 hunch net-2012-05-12-ICML accepted papers and early registration
Introduction: The accepted papers are up in full detail. We are still struggling with the precise program itself, but that’s coming along. Also note the May 13 deadline for early registration and room booking.
6 0.98572063 302 hunch net-2008-05-25-Inappropriate Mathematics for Machine Learning
7 0.98449588 20 hunch net-2005-02-15-ESPgame and image labeling
8 0.98044676 448 hunch net-2011-10-24-2011 ML symposium and the bears
9 0.95143706 90 hunch net-2005-07-07-The Limits of Learning Theory
10 0.93012184 331 hunch net-2008-12-12-Summer Conferences
11 0.89992964 270 hunch net-2007-11-02-The Machine Learning Award goes to …
12 0.89407998 387 hunch net-2010-01-19-Deadline Season, 2010
13 0.87817848 395 hunch net-2010-04-26-Compassionate Reviewing
14 0.86516804 453 hunch net-2012-01-28-Why COLT?
15 0.83571249 65 hunch net-2005-05-02-Reviewing techniques for conferences
16 0.82027936 356 hunch net-2009-05-24-2009 ICML discussion site
17 0.8140713 457 hunch net-2012-02-29-Key Scientific Challenges and the Franklin Symposium
18 0.80232567 216 hunch net-2006-11-02-2006 NIPS workshops
19 0.78672558 46 hunch net-2005-03-24-The Role of Workshops
20 0.77768278 71 hunch net-2005-05-14-NIPS