hunch_net hunch_net-2005 hunch_net-2005-92 knowledge-graph by maker-knowledge-mining
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Introduction: The AAAI conference is running a student blog which looks like a fun experiment.
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same-blog 1 0.99999994 92 hunch net-2005-07-11-AAAI blog
Introduction: The AAAI conference is running a student blog which looks like a fun experiment.
2 0.21216147 96 hunch net-2005-07-21-Six Months
Introduction: This is the 6 month point in the “run a research blog” experiment, so it seems like a good point to take stock and assess. One fundamental question is: “Is it worth it?” The idea of running a research blog will never become widely popular and useful unless it actually aids research. On the negative side, composing ideas for a post and maintaining a blog takes a significant amount of time. On the positive side, the process might yield better research because there is an opportunity for better, faster feedback implying better, faster thinking. My answer at the moment is a provisional “yes”. Running the blog has been incidentally helpful in several ways: It is sometimes educational. example More often, the process of composing thoughts well enough to post simply aids thinking. This has resulted in a couple solutions to problems of interest (and perhaps more over time). If you really want to solve a problem, letting the world know is helpful. This isn’t necessarily
3 0.17522448 174 hunch net-2006-04-27-Conferences, Workshops, and Tutorials
Introduction: This is a reminder that many deadlines for summer conference registration are coming up, and attendance is a very good idea. It’s entirely reasonable for anyone to visit a conference once, even when they don’t have a paper. For students, visiting a conference is almost a ‘must’—there is no where else that a broad cross-section of research is on display. Workshops are also a very good idea. ICML has 11 , KDD has 9 , and AAAI has 19 . Workshops provide an opportunity to get a good understanding of some current area of research. They are probably the forum most conducive to starting new lines of research because they are so interactive. Tutorials are a good way to gain some understanding of a long-standing direction of research. They are generally more coherent than workshops. ICML has 7 and AAAI has 15 .
4 0.16363104 93 hunch net-2005-07-13-“Sister Conference” presentations
Introduction: Some of the “sister conference” presentations at AAAI have been great. Roughly speaking, the conference organizers asked other conference organizers to come give a summary of their conference. Many different AI-related conferences accepted. The presenters typically discuss some of the background and goals of the conference then mention the results from a few papers they liked. This is great because it provides a mechanism to get a digested overview of the work of several thousand researchers—something which is simply available nowhere else. Based on these presentations, it looks like there is a significant component of (and opportunity for) applied machine learning in AIIDE , IUI , and ACL . There was also some discussion of having a super-colocation event similar to FCRC , but centered on AI & Learning. This seems like a fine idea. The field is fractured across so many different conferences that the mixing of a supercolocation seems likely helpful for research.
5 0.15900496 214 hunch net-2006-10-13-David Pennock starts Oddhead
Introduction: his blog on information markets and other research topics .
6 0.15459135 225 hunch net-2007-01-02-Retrospective
7 0.14311343 486 hunch net-2013-07-10-Thoughts on Artificial Intelligence
8 0.13861836 59 hunch net-2005-04-22-New Blog: [Lowerbounds,Upperbounds]
9 0.13808295 383 hunch net-2009-12-09-Inherent Uncertainty
10 0.12555657 166 hunch net-2006-03-24-NLPers
11 0.12059494 65 hunch net-2005-05-02-Reviewing techniques for conferences
12 0.11651219 467 hunch net-2012-06-15-Normal Deviate and the UCSC Machine Learning Summer School
13 0.11534367 350 hunch net-2009-04-23-Jonathan Chang at Slycoder
14 0.11216854 145 hunch net-2005-12-29-Deadline Season
15 0.10928958 418 hunch net-2010-12-02-Traffic Prediction Problem
16 0.1058621 66 hunch net-2005-05-03-Conference attendance is mandatory
17 0.10437208 297 hunch net-2008-04-22-Taking the next step
18 0.10379441 11 hunch net-2005-02-02-Paper Deadlines
19 0.095726721 141 hunch net-2005-12-17-Workshops as Franchise Conferences
20 0.093720265 480 hunch net-2013-03-22-I’m a bandit
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simIndex simValue blogId blogTitle
same-blog 1 0.99556923 92 hunch net-2005-07-11-AAAI blog
Introduction: The AAAI conference is running a student blog which looks like a fun experiment.
2 0.64959466 486 hunch net-2013-07-10-Thoughts on Artificial Intelligence
Introduction: David McAllester starts a blog .
3 0.54806834 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 0.54707217 350 hunch net-2009-04-23-Jonathan Chang at Slycoder
Introduction: Jonathan Chang has a research blog on aspects of machine learning.
5 0.53391618 96 hunch net-2005-07-21-Six Months
Introduction: This is the 6 month point in the “run a research blog” experiment, so it seems like a good point to take stock and assess. One fundamental question is: “Is it worth it?” The idea of running a research blog will never become widely popular and useful unless it actually aids research. On the negative side, composing ideas for a post and maintaining a blog takes a significant amount of time. On the positive side, the process might yield better research because there is an opportunity for better, faster feedback implying better, faster thinking. My answer at the moment is a provisional “yes”. Running the blog has been incidentally helpful in several ways: It is sometimes educational. example More often, the process of composing thoughts well enough to post simply aids thinking. This has resulted in a couple solutions to problems of interest (and perhaps more over time). If you really want to solve a problem, letting the world know is helpful. This isn’t necessarily
6 0.51668304 166 hunch net-2006-03-24-NLPers
7 0.50765854 214 hunch net-2006-10-13-David Pennock starts Oddhead
8 0.49883202 480 hunch net-2013-03-22-I’m a bandit
9 0.49741271 467 hunch net-2012-06-15-Normal Deviate and the UCSC Machine Learning Summer School
10 0.49673295 383 hunch net-2009-12-09-Inherent Uncertainty
11 0.47700816 225 hunch net-2007-01-02-Retrospective
12 0.45769984 174 hunch net-2006-04-27-Conferences, Workshops, and Tutorials
13 0.44401684 182 hunch net-2006-06-05-Server Shift, Site Tweaks, Suggestions?
14 0.43853831 93 hunch net-2005-07-13-“Sister Conference” presentations
15 0.43063888 402 hunch net-2010-07-02-MetaOptimize
16 0.40497914 66 hunch net-2005-05-03-Conference attendance is mandatory
17 0.37413067 283 hunch net-2008-01-07-2008 Summer Machine Learning Conference Schedule
18 0.3050237 418 hunch net-2010-12-02-Traffic Prediction Problem
19 0.3049199 151 hunch net-2006-01-25-1 year
20 0.26769862 296 hunch net-2008-04-21-The Science 2.0 article
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same-blog 1 0.96529651 92 hunch net-2005-07-11-AAAI blog
Introduction: The AAAI conference is running a student blog which looks like a fun experiment.
2 0.78905404 283 hunch net-2008-01-07-2008 Summer Machine Learning Conference Schedule
Introduction: Conference Paper due date Conference Date Location AAAI January 22/23/25/30 July 13-17 Chicago, Illinois ICML Feb 8 July 5-9 Helsinki, Finland COLT Feb 20 July 9-12 Helsinki, Finland KDD Feb 23/29 August 24-27 Las Vegas, Nevada UAI Feb 27/Feb 29 July 9-12 Helsinki, Finland Helsinki is a fun place to visit.
3 0.12464201 220 hunch net-2006-11-27-Continuizing Solutions
Introduction: This post is about a general technique for problem solving which I’ve never seen taught (in full generality), but which I’ve found very useful. Many problems in computer science turn out to be discretely difficult. The best known version of such problems are NP-hard problems, but I mean ‘discretely difficult’ in a much more general way, which I only know how to capture by examples. ERM In empirical risk minimization, you choose a minimum error rate classifier from a set of classifiers. This is NP hard for common sets, but it can be much harder, depending on the set. Experts In the online learning with experts setting, you try to predict well so as to compete with a set of (adversarial) experts. Here the alternating quantifiers of you and an adversary playing out a game can yield a dynamic programming problem that grows exponentially. Policy Iteration The problem with policy iteration is that you learn a new policy with respect to an old policy, which implies that sim
4 0.067697756 268 hunch net-2007-10-19-Second Annual Reinforcement Learning Competition
Introduction: The Second Annual Reinforcement Learning Competition is about to get started. The aim of the competition is to facilitate direct comparisons between various learning methods on important and realistic domains. This year’s event will feature well-known benchmark domains as well as more challenging problems of real-world complexity, such as helicopter control and robot soccer keepaway. The competition begins on November 1st, 2007 when training software is released. Results must be submitted by July 1st, 2008. The competition will culminate in an event at ICML-08 in Helsinki, Finland, at which the winners will be announced. For more information, visit the competition website.
5 0.048108477 145 hunch net-2005-12-29-Deadline Season
Introduction: Many different paper deadlines are coming up soon so I made a little reference table. Out of curiosity, I also computed the interval between submission deadline and conference. Conference Location Date Deadline interval COLT Pittsburgh June 22-25 January 21 152 ICML Pittsburgh June 26-28 January 30/February 6 140 UAI MIT July 13-16 March 9/March 16 119 AAAI Boston July 16-20 February 16/21 145 KDD Philadelphia August 23-26 March 3/March 10 166 It looks like the northeastern US is the big winner as far as location this year.
6 0.036484834 66 hunch net-2005-05-03-Conference attendance is mandatory
7 0.035416842 226 hunch net-2007-01-04-2007 Summer Machine Learning Conferences
8 0.033135314 17 hunch net-2005-02-10-Conferences, Dates, Locations
9 0.032332327 93 hunch net-2005-07-13-“Sister Conference” presentations
10 0.0 1 hunch net-2005-01-19-Why I decided to run a weblog.
11 0.0 2 hunch net-2005-01-24-Holy grails of machine learning?
12 0.0 3 hunch net-2005-01-24-The Humanloop Spectrum of Machine Learning
13 0.0 4 hunch net-2005-01-26-Summer Schools
14 0.0 5 hunch net-2005-01-26-Watchword: Probability
15 0.0 6 hunch net-2005-01-27-Learning Complete Problems
16 0.0 7 hunch net-2005-01-31-Watchword: Assumption
17 0.0 8 hunch net-2005-02-01-NIPS: Online Bayes
18 0.0 9 hunch net-2005-02-01-Watchword: Loss
19 0.0 10 hunch net-2005-02-02-Kolmogorov Complexity and Googling
20 0.0 11 hunch net-2005-02-02-Paper Deadlines