hunch_net hunch_net-2005 hunch_net-2005-15 knowledge-graph by maker-knowledge-mining
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Introduction: Yaroslav Bulatov collects some links to other technical blogs.
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same-blog 1 1.0 15 hunch net-2005-02-08-Some Links
Introduction: Yaroslav Bulatov collects some links to other technical blogs.
2 0.21243118 24 hunch net-2005-02-19-Machine learning reading groups
Introduction: Yaroslav collected an extensive list of machine learning reading groups .
3 0.1430911 216 hunch net-2006-11-02-2006 NIPS workshops
Introduction: I expect the NIPS 2006 workshops to be quite interesting, and recommend going for anyone interested in machine learning research. (Most or all of the workshops webpages can be found two links deep.)
4 0.11527989 246 hunch net-2007-06-13-Not Posting
Introduction: If you have been disappointed by the lack of a post for the last month, consider contributing your own (I’ve been busy+uninspired). Also, keep in mind that there is a community of machine learning blogs (see the sidebar).
5 0.097776882 240 hunch net-2007-04-21-Videolectures.net
Introduction: Davor has been working to setup videolectures.net which is the new site for the many lectures mentioned here . (Tragically, they seem to only be available in windows media format.) I went through my own projects and added a few links to the videos. The day when every result is a set of {paper, slides, video} isn’t quite here yet, but it’s within sight. (For many papers, of course, code is a 4th component.)
6 0.090720281 33 hunch net-2005-02-28-Regularization
7 0.059733555 225 hunch net-2007-01-02-Retrospective
8 0.055840112 354 hunch net-2009-05-17-Server Update
9 0.044027843 296 hunch net-2008-04-21-The Science 2.0 article
10 0.024752196 487 hunch net-2013-07-24-ICML 2012 videos lost
11 0.024072401 98 hunch net-2005-07-27-Not goal metrics
12 0.022706874 382 hunch net-2009-12-09-Future Publication Models @ NIPS
13 0.020879427 453 hunch net-2012-01-28-Why COLT?
14 0.020767208 88 hunch net-2005-07-01-The Role of Impromptu Talks
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16 0.017209399 231 hunch net-2007-02-10-Best Practices for Collaboration
17 0.016588932 134 hunch net-2005-12-01-The Webscience Future
18 0.016362824 70 hunch net-2005-05-12-Math on the Web
19 0.015871143 366 hunch net-2009-08-03-Carbon in Computer Science Research
20 0.014562585 461 hunch net-2012-04-09-ICML author feedback is open
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same-blog 1 0.99922335 15 hunch net-2005-02-08-Some Links
Introduction: Yaroslav Bulatov collects some links to other technical blogs.
2 0.53475994 24 hunch net-2005-02-19-Machine learning reading groups
Introduction: Yaroslav collected an extensive list of machine learning reading groups .
3 0.4875147 240 hunch net-2007-04-21-Videolectures.net
Introduction: Davor has been working to setup videolectures.net which is the new site for the many lectures mentioned here . (Tragically, they seem to only be available in windows media format.) I went through my own projects and added a few links to the videos. The day when every result is a set of {paper, slides, video} isn’t quite here yet, but it’s within sight. (For many papers, of course, code is a 4th component.)
4 0.43446767 487 hunch net-2013-07-24-ICML 2012 videos lost
Introduction: A big ouch—all the videos for ICML 2012 were lost in a shuffle. Rajnish sends the below, but if anyone can help that would be greatly appreciated. —————————————————————————— Sincere apologies to ICML community for loosing 2012 archived videos What happened: In order to publish 2013 videos, we decided to move 2012 videos to another server. We have a weekly backup service from the provider but after removing the videos from the current server, when we tried to retrieve the 2012 videos from backup service, the backup did not work because of provider-specific requirements that we had ignored while removing the data from previous server. What are we doing about this: At this point, we are still looking into raw footage to find if we can retrieve some of the videos, but following are the steps we are taking to make sure this does not happen again in future: (1) We are going to create a channel on Vimeo (and potentially on YouTube) and we will publish there the p-in-p- or slide-vers
5 0.39597294 261 hunch net-2007-08-28-Live ML Class
Introduction: Davor and Chunnan point out that MLSS 2007 in Tuebingen has live video for the majority of the world that is not there (heh).
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8 0.33487588 216 hunch net-2006-11-02-2006 NIPS workshops
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11 0.30846745 479 hunch net-2013-01-31-Remote large scale learning class participation
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15 0.26222134 354 hunch net-2009-05-17-Server Update
16 0.25923911 483 hunch net-2013-06-10-The Large Scale Learning class notes
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20 0.22058335 266 hunch net-2007-10-15-NIPS workshops extended to 3 days
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same-blog 1 1.0 15 hunch net-2005-02-08-Some Links
Introduction: Yaroslav Bulatov collects some links to other technical blogs.
2 0.52774918 415 hunch net-2010-10-28-NY ML Symposium 2010
Introduction: About 200 people attended the 2010 NYAS ML Symposium this year. (It was about 170 last year .) I particularly enjoyed several talks. Yann has a new live demo of (limited) real-time object recognition learning. Sanjoy gave a fairly convincing and comprehensible explanation of why a modified form of single-linkage clustering is consistent in higher dimensions, and why consistency is a critical feature for clustering algorithms. I’m curious how well this algorithm works in practice. Matt Hoffman ‘s poster covering online LDA seemed pretty convincing to me as an algorithmic improvement. This year, we allocated more time towards posters & poster spotlights. For next year, we are considering some further changes. The format has traditionally been 4 invited Professor speakers, with posters and poster spotlight for students. Demand from other parties to participate is growing, for example from postdocs and startups in the area. Another growing concern is the fa
3 0.48916572 188 hunch net-2006-06-30-ICML papers
Introduction: Here are some ICML papers which interested me. Arindam Banerjee had a paper which notes that PAC-Bayes bounds, a core theorem in online learning, and the optimality of Bayesian learning statements share a core inequality in their proof. Pieter Abbeel , Morgan Quigley and Andrew Y. Ng have a paper discussing RL techniques for learning given a bad (but not too bad) model of the world. Nina Balcan and Avrim Blum have a paper which discusses how to learn given a similarity function rather than a kernel. A similarity function requires less structure than a kernel, implying that a learning algorithm using a similarity function might be applied in situations where no effective kernel is evident. Nathan Ratliff , Drew Bagnell , and Marty Zinkevich have a paper describing an algorithm which attempts to fuse A * path planning with learning of transition costs based on human demonstration. Papers (2), (3), and (4), all seem like an initial pass at solving in
4 0.46500826 153 hunch net-2006-02-02-Introspectionism as a Disease
Introduction: In the AI-related parts of machine learning, it is often tempting to examine how you do things in order to imagine how a machine should do things. This is introspection, and it can easily go awry. I will call introspection gone awry introspectionism. Introspectionism is almost unique to AI (and the AI-related parts of machine learning) and it can lead to huge wasted effort in research. It’s easiest to show how introspectionism arises by an example. Suppose we want to solve the problem of navigating a robot from point A to point B given a camera. Then, the following research action plan might seem natural when you examine your own capabilities: Build an edge detector for still images. Build an object recognition system given the edge detector. Build a system to predict distance and orientation to objects given the object recognition system. Build a system to plan a path through the scene you construct from {object identification, distance, orientation} predictions.
5 0.46233803 457 hunch net-2012-02-29-Key Scientific Challenges and the Franklin Symposium
Introduction: For graduate students, the Yahoo! Key Scientific Challenges program including in machine learning is on again, due March 9 . The application is easy and the $5K award is high quality “no strings attached” funding. Consider submitting. Those in Washington DC, Philadelphia, and New York, may consider attending the Franklin Institute Symposium April 25 which has several speakers and an award for V . Attendance is free with an RSVP.
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19 0.0 1 hunch net-2005-01-19-Why I decided to run a weblog.
20 0.0 2 hunch net-2005-01-24-Holy grails of machine learning?