hunch_net hunch_net-2009 hunch_net-2009-372 knowledge-graph by maker-knowledge-mining

372 hunch net-2009-09-29-Machine Learning Protests at the G20


meta infos for this blog

Source: html

Introduction: The machine learning department at CMU turned out en masse to protest the G20 summit in Pittsburgh. Arthur Gretton uploaded some great photos covering the event


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 The machine learning department at CMU turned out en masse to protest the G20 summit in Pittsburgh. [sent-1, score-1.454]


similar blogs computed by tfidf model

tfidf for this blog:

wordName wordTfidf (topN-words)

[('en', 0.395), ('arthur', 0.395), ('gretton', 0.395), ('summit', 0.345), ('turned', 0.329), ('department', 0.305), ('cmu', 0.279), ('covering', 0.255), ('event', 0.226), ('great', 0.12), ('machine', 0.057), ('learning', 0.023)]

similar blogs list:

simIndex simValue blogId blogTitle

same-blog 1 1.0 372 hunch net-2009-09-29-Machine Learning Protests at the G20

Introduction: The machine learning department at CMU turned out en masse to protest the G20 summit in Pittsburgh. Arthur Gretton uploaded some great photos covering the event

2 0.19883913 321 hunch net-2008-10-19-NIPS 2008 workshop on Kernel Learning

Introduction: We’d like to invite hunch.net readers to participate in the NIPS 2008 workshop on kernel learning. While the main focus is on automatically learning kernels from data, we are also also looking at the broader questions of feature selection, multi-task learning and multi-view learning. There are no restrictions on the learning problem being addressed (regression, classification, etc), and both theoretical and applied work will be considered. The deadline for submissions is October 24 . More detail can be found here . Corinna Cortes, Arthur Gretton, Gert Lanckriet, Mehryar Mohri, Afshin Rostamizadeh

3 0.14703214 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 .

4 0.11124834 228 hunch net-2007-01-15-The Machine Learning Department

Introduction: Carnegie Mellon School of Computer Science has the first academic Machine Learning department . This department already existed as the Center for Automated Learning and Discovery , but recently changed it’s name. The reason for changing the name is obvious: very few people think of themselves as “Automated Learner and Discoverers”, but there are number of people who think of themselves as “Machine Learners”. Machine learning is both more succinct and recognizable—good properties for a name. A more interesting question is “Should there be a Machine Learning Department?”. Tom Mitchell has a relevant whitepaper claiming that machine learning is answering a different question than other fields or departments. The fundamental debate here is “Is machine learning different from statistics?” At a cultural level, there is no real debate: they are different. Machine learning is characterized by several very active large peer reviewed conferences, operating in a computer

5 0.10953346 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.

6 0.063944496 138 hunch net-2005-12-09-Some NIPS papers

7 0.063798234 82 hunch net-2005-06-17-Reopening RL->Classification

8 0.062361952 119 hunch net-2005-10-08-We have a winner

9 0.060113586 410 hunch net-2010-09-17-New York Area Machine Learning Events

10 0.049514163 369 hunch net-2009-08-27-New York Area Machine Learning Events

11 0.049426451 268 hunch net-2007-10-19-Second Annual Reinforcement Learning Competition

12 0.04770752 302 hunch net-2008-05-25-Inappropriate Mathematics for Machine Learning

13 0.041225921 455 hunch net-2012-02-20-Berkeley Streaming Data Workshop

14 0.03955787 449 hunch net-2011-11-26-Giving Thanks

15 0.039202355 477 hunch net-2013-01-01-Deep Learning 2012

16 0.037912723 384 hunch net-2009-12-24-Top graduates this season

17 0.035861634 50 hunch net-2005-04-01-Basic computer science research takes a hit

18 0.035541102 93 hunch net-2005-07-13-“Sister Conference” presentations

19 0.031581406 14 hunch net-2005-02-07-The State of the Reduction

20 0.031183425 159 hunch net-2006-02-27-The Peekaboom Dataset


similar blogs computed by lsi model

lsi for this blog:

topicId topicWeight

[(0, 0.029), (1, -0.007), (2, -0.044), (3, -0.003), (4, -0.0), (5, 0.01), (6, 0.019), (7, -0.015), (8, -0.005), (9, -0.063), (10, 0.01), (11, -0.012), (12, 0.026), (13, -0.014), (14, -0.038), (15, -0.008), (16, -0.001), (17, 0.022), (18, -0.022), (19, 0.023), (20, -0.003), (21, 0.027), (22, -0.025), (23, 0.021), (24, 0.028), (25, -0.052), (26, 0.01), (27, 0.076), (28, -0.033), (29, 0.038), (30, 0.011), (31, 0.052), (32, -0.034), (33, 0.006), (34, -0.083), (35, 0.049), (36, 0.016), (37, -0.08), (38, 0.128), (39, 0.055), (40, 0.008), (41, 0.041), (42, 0.082), (43, 0.092), (44, -0.016), (45, -0.064), (46, 0.077), (47, 0.019), (48, 0.122), (49, -0.045)]

similar blogs list:

simIndex simValue blogId blogTitle

same-blog 1 0.94408882 372 hunch net-2009-09-29-Machine Learning Protests at the G20

Introduction: The machine learning department at CMU turned out en masse to protest the G20 summit in Pittsburgh. Arthur Gretton uploaded some great photos covering the event

2 0.63630307 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 0.5398246 313 hunch net-2008-08-18-Radford Neal starts a blog

Introduction: here on statistics, ML, CS, and other things he knows well.

4 0.49250516 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 0.47401235 228 hunch net-2007-01-15-The Machine Learning Department

Introduction: Carnegie Mellon School of Computer Science has the first academic Machine Learning department . This department already existed as the Center for Automated Learning and Discovery , but recently changed it’s name. The reason for changing the name is obvious: very few people think of themselves as “Automated Learner and Discoverers”, but there are number of people who think of themselves as “Machine Learners”. Machine learning is both more succinct and recognizable—good properties for a name. A more interesting question is “Should there be a Machine Learning Department?”. Tom Mitchell has a relevant whitepaper claiming that machine learning is answering a different question than other fields or departments. The fundamental debate here is “Is machine learning different from statistics?” At a cultural level, there is no real debate: they are different. Machine learning is characterized by several very active large peer reviewed conferences, operating in a computer

6 0.46988028 271 hunch net-2007-11-05-CMU wins DARPA Urban Challenge

7 0.44787815 268 hunch net-2007-10-19-Second Annual Reinforcement Learning Competition

8 0.42165184 321 hunch net-2008-10-19-NIPS 2008 workshop on Kernel Learning

9 0.3824544 414 hunch net-2010-10-17-Partha Niyogi has died

10 0.35643369 232 hunch net-2007-02-11-24

11 0.34135902 297 hunch net-2008-04-22-Taking the next step

12 0.33603141 455 hunch net-2012-02-20-Berkeley Streaming Data Workshop

13 0.33388048 270 hunch net-2007-11-02-The Machine Learning Award goes to …

14 0.32903576 412 hunch net-2010-09-28-Machined Learnings

15 0.32365829 460 hunch net-2012-03-24-David Waltz

16 0.31965974 13 hunch net-2005-02-04-JMLG

17 0.31606075 105 hunch net-2005-08-23-(Dis)similarities between academia and open source programmers

18 0.31095201 212 hunch net-2006-10-04-Health of Conferences Wiki

19 0.30493653 290 hunch net-2008-02-27-The Stats Handicap

20 0.29486868 410 hunch net-2010-09-17-New York Area Machine Learning Events


similar blogs computed by lda model

lda for this blog:

topicId topicWeight

[(27, 0.019), (47, 0.744)]

similar blogs list:

simIndex simValue blogId blogTitle

same-blog 1 0.94854289 372 hunch net-2009-09-29-Machine Learning Protests at the G20

Introduction: The machine learning department at CMU turned out en masse to protest the G20 summit in Pittsburgh. Arthur Gretton uploaded some great photos covering the event

2 0.37652043 329 hunch net-2008-11-28-A Bumper Crop of Machine Learning Graduates

Introduction: My impression is that this is a particularly strong year for machine learning graduates. Here’s my short list of the strong graduates I know. Analpha (for perversity’s sake) by last name: Jenn Wortmann . When Jenn visited us for the summer, she had one , two , three , four papers. That is typical—she’s smart, capable, and follows up many directions of research. I believe approximately all of her many papers are on different subjects. Ruslan Salakhutdinov . A Science paper on bijective dimensionality reduction , mastered and improved on deep belief nets which seems like an important flavor of nonlinear learning, and in my experience he’s very fast, capable and creative at problem solving. Marc’Aurelio Ranzato . I haven’t spoken with Marc very much, but he had a great visit at Yahoo! this summer, and has an impressive portfolio of applications and improvements on convolutional neural networks and other deep learning algorithms. Lihong Li . Lihong developed the

3 0.1941162 320 hunch net-2008-10-14-Who is Responsible for a Bad Review?

Introduction: Although I’m greatly interested in machine learning, I think it must be admitted that there is a large amount of low quality logic being used in reviews. The problem is bad enough that sometimes I wonder if the Byzantine generals limit has been exceeded. For example, I’ve seen recent reviews where the given reasons for rejecting are: [ NIPS ] Theorem A is uninteresting because Theorem B is uninteresting. [ UAI ] When you learn by memorization, the problem addressed is trivial. [NIPS] The proof is in the appendix. [NIPS] This has been done before. (… but not giving any relevant citations) Just for the record I want to point out what’s wrong with these reviews. A future world in which such reasons never come up again would be great, but I’m sure these errors will be committed many times more in the future. This is nonsense. A theorem should be evaluated based on it’s merits, rather than the merits of another theorem. Learning by memorization requires an expon

4 0.1837655 36 hunch net-2005-03-05-Funding Research

Introduction: The funding of research (and machine learning research) is an issue which seems to have become more significant in the United States over the last decade. The word “research” is applied broadly here to science, mathematics, and engineering. There are two essential difficulties with funding research: Longshot Paying a researcher is often a big gamble. Most research projects don’t pan out, but a few big payoffs can make it all worthwhile. Information Only Much of research is about finding the right way to think about or do something. The Longshot difficulty means that there is high variance in payoffs. This can be compensated for by funding many different research projects, reducing variance. The Information-Only difficulty means that it’s hard to extract a profit directly from many types of research, so companies have difficulty justifying basic research. (Patents are a mechanism for doing this. They are often extraordinarily clumsy or simply not applicable.) T

5 0.016510218 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.

6 0.016510218 465 hunch net-2012-05-12-ICML accepted papers and early registration

7 0.016267586 472 hunch net-2012-08-27-NYAS ML 2012 and ICML 2013

8 0.015842073 271 hunch net-2007-11-05-CMU wins DARPA Urban Challenge

9 0.015832249 446 hunch net-2011-10-03-Monday announcements

10 0.015599848 302 hunch net-2008-05-25-Inappropriate Mathematics for Machine Learning

11 0.015563751 20 hunch net-2005-02-15-ESPgame and image labeling

12 0.01549017 448 hunch net-2011-10-24-2011 ML symposium and the bears

13 0.014761313 331 hunch net-2008-12-12-Summer Conferences

14 0.014744629 90 hunch net-2005-07-07-The Limits of Learning Theory

15 0.014226571 387 hunch net-2010-01-19-Deadline Season, 2010

16 0.013540486 270 hunch net-2007-11-02-The Machine Learning Award goes to …

17 0.013144366 395 hunch net-2010-04-26-Compassionate Reviewing

18 0.012955079 453 hunch net-2012-01-28-Why COLT?

19 0.012896485 226 hunch net-2007-01-04-2007 Summer Machine Learning Conferences

20 0.012760076 422 hunch net-2011-01-16-2011 Summer Conference Deadline Season