hunch_net hunch_net-2005 hunch_net-2005-145 knowledge-graph by maker-knowledge-mining
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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.
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same-blog 1 0.99999994 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.
2 0.34763205 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 0.29824516 387 hunch net-2010-01-19-Deadline Season, 2010
Introduction: Many conference deadlines are coming soon. Deadline Double Blind / Author Feedback Time/Place ICML January 18((workshops) / February 1 (Papers) / February 13 (Tutorials) Y/Y Haifa, Israel, June 21-25 KDD February 1(Workshops) / February 2&5 (Papers) / February 26 (Tutorials & Panels)) / April 17 (Demos) N/S Washington DC, July 25-28 COLT January 18 (Workshops) / February 19 (Papers) N/S Haifa, Israel, June 25-29 UAI March 11 (Papers) N?/Y Catalina Island, California, July 8-11 ICML continues to experiment with the reviewing process, although perhaps less so than last year. The S “sort-of” for COLT is because author feedback occurs only after decisions are made. KDD is notable for being the most comprehensive in terms of {Tutorials, Workshops, Challenges, Panels, Papers (two tracks), Demos}. The S for KDD is because there is sometimes author feedback at the decision of the SPC. The (past) January 18 de
4 0.29184899 422 hunch net-2011-01-16-2011 Summer Conference Deadline Season
Introduction: Machine learning always welcomes the new year with paper deadlines for summer conferences. This year, we have: Conference Paper Deadline When/Where Double blind? Author Feedback? Notes ICML February 1 June 28-July 2, Bellevue, Washington, USA Y Y Weak colocation with ACL COLT February 11 July 9-July 11, Budapest, Hungary N N colocated with FOCM KDD February 11/18 August 21-24, San Diego, California, USA N N UAI March 18 July 14-17, Barcelona, Spain Y N The larger conferences are on the west coast in the United States, while the smaller ones are in Europe.
5 0.27146623 17 hunch net-2005-02-10-Conferences, Dates, Locations
Introduction: Conference Locate Date COLT Bertinoro, Italy June 27-30 AAAI Pittsburgh, PA, USA July 9-13 UAI Edinburgh, Scotland July 26-29 IJCAI Edinburgh, Scotland July 30 – August 5 ICML Bonn, Germany August 7-11 KDD Chicago, IL, USA August 21-24 The big winner this year is Europe. This is partly a coincidence, and partly due to the general internationalization of science over the last few years. With cuts to basic science in the US and increased hassle for visitors, conferences outside the US become more attractive. Europe and Australia/New Zealand are the immediate winners because they have the science, infrastructure, and english in place. China and India are possible future winners.
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same-blog 1 0.98843253 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.
2 0.86058784 422 hunch net-2011-01-16-2011 Summer Conference Deadline Season
Introduction: Machine learning always welcomes the new year with paper deadlines for summer conferences. This year, we have: Conference Paper Deadline When/Where Double blind? Author Feedback? Notes ICML February 1 June 28-July 2, Bellevue, Washington, USA Y Y Weak colocation with ACL COLT February 11 July 9-July 11, Budapest, Hungary N N colocated with FOCM KDD February 11/18 August 21-24, San Diego, California, USA N N UAI March 18 July 14-17, Barcelona, Spain Y N The larger conferences are on the west coast in the United States, while the smaller ones are in Europe.
3 0.80768734 226 hunch net-2007-01-04-2007 Summer Machine Learning Conferences
Introduction: It’s conference season once again. Conference Due? When? Where? double blind? author feedback? Workshops? AAAI February 1/6 (and 27) July 22-26 Vancouver, British Columbia Yes Yes Done UAI February 28/March 2 July 19-22 Vancouver, British Columbia No No No COLT January 16 June 13-15 San Diego, California (with FCRC ) No No No ICML February 7/9 June 20-24 Corvallis, Oregon Yes Yes February 16 KDD February 23/28 August 12-15 San Jose, California Yes No? February 28 The geowinner this year is the west coast of North America. Last year ‘s geowinner was the Northeastern US, and the year before it was mostly Europe. It’s notable how tightly the conferences cluster, even when they don’t colocate.
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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 0.72780561 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.
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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.
2 0.92856443 107 hunch net-2005-09-05-Site Update
Introduction: I tweaked the site in a number of ways today, including: Updating to WordPress 1.5. Installing and heavily tweaking the Geekniche theme. Update: I switched back to a tweaked version of the old theme. Adding the Customizable Post Listings plugin. Installing the StatTraq plugin. Updating some of the links. I particularly recommend looking at the computer research policy blog. Adding threaded comments . This doesn’t thread old comments obviously, but the extra structure may be helpful for new ones. Overall, I think this is an improvement, and it addresses a few of my earlier problems . If you have any difficulties or anything seems “not quite right”, please speak up. A few other tweaks to the site may happen in the near future.
3 0.90037531 16 hunch net-2005-02-09-Intuitions from applied learning
Introduction: Since learning is far from an exact science, it’s good to pay attention to basic intuitions of applied learning. Here are a few I’ve collected. Integration In Bayesian learning, the posterior is computed by an integral, and the optimal thing to do is to predict according to this integral. This phenomena seems to be far more general. Bagging, Boosting, SVMs, and Neural Networks all take advantage of this idea to some extent. The phenomena is more general: you can average over many different classification predictors to improve performance. Sources: Zoubin , Caruana Differentiation Different pieces of an average should differentiate to achieve good performance by different methods. This is know as the ‘symmetry breaking’ problem for neural networks, and it’s why weights are initialized randomly. Boosting explicitly attempts to achieve good differentiation by creating new, different, learning problems. Sources: Yann LeCun , Phil Long Deep Representation Ha
4 0.88837087 56 hunch net-2005-04-14-Families of Learning Theory Statements
Introduction: The diagram above shows a very broad viewpoint of learning theory. arrow Typical statement Examples Past->Past Some prediction algorithm A does almost as well as any of a set of algorithms. Weighted Majority Past->Future Assuming independent samples, past performance predicts future performance. PAC analysis, ERM analysis Future->Future Future prediction performance on subproblems implies future prediction performance using algorithm A . ECOC, Probing A basic question is: Are there other varieties of statements of this type? Avrim noted that there are also “arrows between arrows”: generic methods for transforming between Past->Past statements and Past->Future statements. Are there others?
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Introduction: One thing common to much research is that the researcher must be the first person ever to have some thought. How do you think of something that has never been thought of? There seems to be no methodical manner of doing this, but there are some tricks. The easiest method is to just have some connection come to you. There is a trick here however: you should write it down and fill out the idea immediately because it can just as easily go away. A harder method is to set aside a block of time and simply think about an idea. Distraction elimination is essential here because thinking about the unthought is hard work which your mind will avoid. Another common method is in conversation. Sometimes the process of verbalizing implies new ideas come up and sometimes whoever you are talking to replies just the right way. This method is dangerous though—you must speak to someone who helps you think rather than someone who occupies your thoughts. Try to rephrase the problem so the a
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