hunch_net hunch_net-2007 hunch_net-2007-254 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: Mark Reid did a post on ICML trends that I found interesting.
sentIndex sentText sentNum sentScore
1 Mark Reid did a post on ICML trends that I found interesting. [sent-1, score-1.055]
wordName wordTfidf (topN-words)
[('trends', 0.559), ('reid', 0.518), ('mark', 0.463), ('post', 0.249), ('found', 0.247), ('icml', 0.209), ('interesting', 0.197)]
simIndex simValue blogId blogTitle
same-blog 1 0.99999994 254 hunch net-2007-07-12-ICML Trends
Introduction: Mark Reid did a post on ICML trends that I found interesting.
2 0.3840287 305 hunch net-2008-06-30-ICML has a comment system
Introduction: Mark Reid has stepped up and created a comment system for ICML papers which Greger Linden has tightly integrated. My understanding is that Mark spent quite a bit of time on the details, and there are some cool features like working latex math mode. This is an excellent chance for the ICML community to experiment with making ICML year-round, so I hope it works out. Please do consider experimenting with it.
3 0.28168201 401 hunch net-2010-06-20-2010 ICML discussion site
Introduction: A substantial difficulty with the 2009 and 2008 ICML discussion system was a communication vacuum, where authors were not informed of comments, and commenters were not informed of responses to their comments without explicit monitoring. Mark Reid has setup a new discussion system for 2010 with the goal of addressing this. Mark didn’t want to make it to intrusive, so you must opt-in. As an author, find your paper and “Subscribe by email” to the comments. As a commenter, you have the option of providing an email for follow-up notification.
4 0.24241388 356 hunch net-2009-05-24-2009 ICML discussion site
Introduction: Mark Reid has setup a discussion site for ICML papers again this year and Monica Dinculescu has linked it in from the ICML site. Last year’s attempt appears to have been an acceptable but not wild success as a little bit of fruitful discussion occurred. I’m hoping this year will be a bit more of a success—please don’t be shy I’d like to also point out that ICML ‘s early registration deadline has a few hours left, while UAI ‘s and COLT ‘s are in a week.
5 0.12554201 468 hunch net-2012-06-29-ICML survey and comments
Introduction: Just about nothing could keep me from attending ICML , except for Dora who arrived on Monday. Consequently, I have only secondhand reports that the conference is going well. For those who are remote (like me) or after the conference (like everyone), Mark Reid has setup the ICML discussion site where you can comment on any paper or subscribe to papers. Authors are automatically subscribed to their own papers, so it should be possible to have a discussion significantly after the fact, as people desire. We also conducted a survey before the conference and have the survey results now. This can be compared with the ICML 2010 survey results . Looking at the comparable questions, we can sometimes order the answers to have scores ranging from 0 to 3 or 0 to 4 with 3 or 4 being best and 0 worst, then compute the average difference between 2012 and 2010. Glancing through them, I see: Most people found the papers they reviewed a good fit for their expertise (-.037 w.r.t 20
6 0.10273871 361 hunch net-2009-06-24-Interesting papers at UAICMOLT 2009
7 0.098660052 364 hunch net-2009-07-11-Interesting papers at KDD
8 0.083765417 452 hunch net-2012-01-04-Why ICML? and the summer conferences
9 0.081264623 340 hunch net-2009-01-28-Nielsen’s talk
10 0.073573776 216 hunch net-2006-11-02-2006 NIPS workshops
11 0.06539163 225 hunch net-2007-01-02-Retrospective
12 0.062907666 306 hunch net-2008-07-02-Proprietary Data in Academic Research?
13 0.059097134 403 hunch net-2010-07-18-ICML & COLT 2010
14 0.056199871 117 hunch net-2005-10-03-Not ICML
15 0.055416159 454 hunch net-2012-01-30-ICML Posters and Scope
16 0.054430436 472 hunch net-2012-08-27-NYAS ML 2012 and ICML 2013
17 0.050190005 453 hunch net-2012-01-28-Why COLT?
18 0.048821613 37 hunch net-2005-03-08-Fast Physics for Learning
19 0.048014488 234 hunch net-2007-02-22-Create Your Own ICML Workshop
20 0.047316644 143 hunch net-2005-12-27-Automated Labeling
topicId topicWeight
[(0, 0.056), (1, -0.08), (2, 0.02), (3, -0.053), (4, 0.04), (5, -0.008), (6, 0.001), (7, -0.102), (8, -0.013), (9, -0.013), (10, -0.02), (11, -0.002), (12, -0.217), (13, 0.084), (14, 0.208), (15, 0.018), (16, -0.268), (17, -0.196), (18, -0.024), (19, 0.062), (20, 0.052), (21, -0.096), (22, -0.094), (23, 0.11), (24, -0.021), (25, -0.151), (26, 0.029), (27, -0.136), (28, 0.068), (29, 0.056), (30, -0.114), (31, -0.06), (32, 0.007), (33, 0.105), (34, -0.066), (35, 0.006), (36, -0.069), (37, -0.047), (38, 0.033), (39, 0.018), (40, 0.111), (41, -0.015), (42, -0.03), (43, 0.084), (44, 0.08), (45, 0.076), (46, -0.031), (47, 0.018), (48, 0.103), (49, -0.018)]
simIndex simValue blogId blogTitle
same-blog 1 0.99276471 254 hunch net-2007-07-12-ICML Trends
Introduction: Mark Reid did a post on ICML trends that I found interesting.
2 0.89352822 305 hunch net-2008-06-30-ICML has a comment system
Introduction: Mark Reid has stepped up and created a comment system for ICML papers which Greger Linden has tightly integrated. My understanding is that Mark spent quite a bit of time on the details, and there are some cool features like working latex math mode. This is an excellent chance for the ICML community to experiment with making ICML year-round, so I hope it works out. Please do consider experimenting with it.
3 0.85408622 401 hunch net-2010-06-20-2010 ICML discussion site
Introduction: A substantial difficulty with the 2009 and 2008 ICML discussion system was a communication vacuum, where authors were not informed of comments, and commenters were not informed of responses to their comments without explicit monitoring. Mark Reid has setup a new discussion system for 2010 with the goal of addressing this. Mark didn’t want to make it to intrusive, so you must opt-in. As an author, find your paper and “Subscribe by email” to the comments. As a commenter, you have the option of providing an email for follow-up notification.
4 0.73156708 356 hunch net-2009-05-24-2009 ICML discussion site
Introduction: Mark Reid has setup a discussion site for ICML papers again this year and Monica Dinculescu has linked it in from the ICML site. Last year’s attempt appears to have been an acceptable but not wild success as a little bit of fruitful discussion occurred. I’m hoping this year will be a bit more of a success—please don’t be shy I’d like to also point out that ICML ‘s early registration deadline has a few hours left, while UAI ‘s and COLT ‘s are in a week.
5 0.60456485 117 hunch net-2005-10-03-Not ICML
Introduction: Alex Smola showed me this ICML 2006 webpage. This is NOT the ICML we know, but rather some people at “Enformatika”. Investigation shows that they registered with an anonymous yahoo email account from dotregistrar.com the “Home of the $6.79 wholesale domain!” and their nameservers are by Turkticaret , a Turkish internet company. It appears the website has since been altered to “ ICNL ” (the above link uses the google cache). They say that imitation is the sincerest form of flattery, so the organizers of the real ICML 2006 must feel quite flattered.
6 0.4898856 468 hunch net-2012-06-29-ICML survey and comments
7 0.45045999 37 hunch net-2005-03-08-Fast Physics for Learning
8 0.3707847 472 hunch net-2012-08-27-NYAS ML 2012 and ICML 2013
9 0.3561092 246 hunch net-2007-06-13-Not Posting
10 0.34885928 487 hunch net-2013-07-24-ICML 2012 videos lost
11 0.34719926 364 hunch net-2009-07-11-Interesting papers at KDD
12 0.33103952 403 hunch net-2010-07-18-ICML & COLT 2010
13 0.33078468 340 hunch net-2009-01-28-Nielsen’s talk
14 0.31019315 361 hunch net-2009-06-24-Interesting papers at UAICMOLT 2009
15 0.3094452 452 hunch net-2012-01-04-Why ICML? and the summer conferences
16 0.288445 447 hunch net-2011-10-10-ML Symposium and ICML details
17 0.27245674 433 hunch net-2011-04-23-ICML workshops due
18 0.27038616 65 hunch net-2005-05-02-Reviewing techniques for conferences
19 0.22619717 188 hunch net-2006-06-30-ICML papers
20 0.22458236 354 hunch net-2009-05-17-Server Update
topicId topicWeight
[(79, 0.712)]
simIndex simValue blogId blogTitle
same-blog 1 0.93574798 254 hunch net-2007-07-12-ICML Trends
Introduction: Mark Reid did a post on ICML trends that I found interesting.
2 0.47548622 354 hunch net-2009-05-17-Server Update
Introduction: The hunch.net server has been updated. I’ve taken the opportunity to upgrade the version of wordpress which caused cascading changes. Old threaded comments are now flattened. The system we used to use ( Brian’s threaded comments ) appears incompatible with the new threading system built into wordpress. I haven’t yet figured out a workaround. I setup a feedburner account . I added an RSS aggregator for both Machine Learning and other research blogs that I like to follow. This is something that I’ve wanted to do for awhile. Many other minor changes in font and format, with some help from Alina . If you have any suggestions for site tweaks, please speak up.
3 0.39725241 248 hunch net-2007-06-19-How is Compressed Sensing going to change Machine Learning ?
Introduction: Compressed Sensing (CS) is a new framework developed by Emmanuel Candes , Terry Tao and David Donoho . To summarize, if you acquire a signal in some basis that is incoherent with the basis in which you know the signal to be sparse in, it is very likely you will be able to reconstruct the signal from these incoherent projections. Terry Tao, the recent Fields medalist , does a very nice job at explaining the framework here . He goes further in the theory description in this post where he mentions the central issue of the Uniform Uncertainty Principle. It so happens that random projections are on average incoherent, within the UUP meaning, with most known basis (sines, polynomials, splines, wavelets, curvelets …) and are therefore an ideal basis for Compressed Sensing. [ For more in-depth information on the subject, the Rice group has done a very good job at providing a central library of papers relevant to the growing subject: http://www.dsp.ece.rice.edu/cs/ ] The Machine
4 0.38713396 162 hunch net-2006-03-09-Use of Notation
Introduction: For most people, a mathematical notation is like a language: you learn it and stick with it. For people doing mathematical research, however, this is not enough: they must design new notations for new problems. The design of good notation is both hard and worthwhile since a bad initial notation can retard a line of research greatly. Before we had mathematical notation, equations were all written out in language. Since words have multiple meanings and variable precedences, long equations written out in language can be extraordinarily difficult and sometimes fundamentally ambiguous. A good representative example of this is the legalese in the tax code. Since we want greater precision and clarity, we adopt mathematical notation. One fundamental thing to understand about mathematical notation, is that humans as logic verifiers, are barely capable. This is the fundamental reason why one notation can be much better than another. This observation is easier to miss than you might
5 0.28149578 27 hunch net-2005-02-23-Problem: Reinforcement Learning with Classification
Introduction: At an intuitive level, the question here is “Can reinforcement learning be solved with classification?” Problem Construct a reinforcement learning algorithm with near-optimal expected sum of rewards in the direct experience model given access to a classifier learning algorithm which has a small error rate or regret on all posed classification problems. The definition of “posed” here is slightly murky. I consider a problem “posed” if there is an algorithm for constructing labeled classification examples. Past Work There exists a reduction of reinforcement learning to classification given a generative model. A generative model is an inherently stronger assumption than the direct experience model. Other work on learning reductions may be important. Several algorithms for solving reinforcement learning in the direct experience model exist. Most, such as E 3 , Factored-E 3 , and metric-E 3 and Rmax require that the observation be the state. Recent work
6 0.21898967 423 hunch net-2011-02-02-User preferences for search engines
7 0.21614593 204 hunch net-2006-08-28-Learning Theory standards for NIPS 2006
8 0.089761332 356 hunch net-2009-05-24-2009 ICML discussion site
9 0.043983769 169 hunch net-2006-04-05-What is state?
10 0.030421989 156 hunch net-2006-02-11-Yahoo’s Learning Problems.
11 0.024393193 361 hunch net-2009-06-24-Interesting papers at UAICMOLT 2009
12 0.0 1 hunch net-2005-01-19-Why I decided to run a weblog.
13 0.0 2 hunch net-2005-01-24-Holy grails of machine learning?
14 0.0 3 hunch net-2005-01-24-The Humanloop Spectrum of Machine Learning
15 0.0 4 hunch net-2005-01-26-Summer Schools
16 0.0 5 hunch net-2005-01-26-Watchword: Probability
17 0.0 6 hunch net-2005-01-27-Learning Complete Problems
18 0.0 7 hunch net-2005-01-31-Watchword: Assumption
19 0.0 8 hunch net-2005-02-01-NIPS: Online Bayes
20 0.0 9 hunch net-2005-02-01-Watchword: Loss