hunch_net hunch_net-2007 hunch_net-2007-240 knowledge-graph by maker-knowledge-mining
Source: html
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.)
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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.)
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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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Introduction: The large scale machine learning class I taught with Yann LeCun has finished. As I expected, it took quite a bit of time . We had about 25 people attending in person on average and 400 regularly watching the recorded lectures which is substantially more sustained interest than I expected for an advanced ML class. We also had some fun with class projects—I’m hopeful that several will eventually turn into papers. I expect there are a number of professors interested in lecturing on this and related topics. Everyone will have their personal taste in subjects of course, but hopefully there will be some convergence to common course materials as well. To help with this, I am making the sources to my presentations available . Feel free to use/improve/embelish/ridicule/etc… in the pursuit of the perfect course.
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Introduction: I just created version 5.1 of vowpal wabbit . This almost entirely a bugfix release, so it’s an easy upgrade from v5.0. In addition: There is now a mailing list , which I and several other developers are subscribed to. The main website has shifted to the wiki on github. This means that anyone with a github account can now edit it. I’m planning to give a tutorial tomorrow on it at eHarmony / the LA machine learning meetup at 10am. Drop by if you’re interested. The status of VW amongst other open source projects has changed. When VW first came out, it was relatively unique amongst existing projects in terms of features. At this point, many other projects have started to appreciate the value of the design choices here. This includes: Mahout , which now has an SGD implementation. Shogun , where Soeren is keen on incorporating features . LibLinear , where they won the KDD best paper award for out-of-core learning . This is expected—any open sourc
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Introduction: Yann and I have arranged so that people who are interested in our large scale machine learning class and not able to attend in person can follow along via two methods. Videos will be posted with about a 1 day delay on techtalks . This is a side-by-side capture of video+slides from Weyond . We are experimenting with Piazza as a discussion forum. Anyone is welcome to subscribe to Piazza and ask questions there, where I will be monitoring things. update2 : Sign up here . The first lecture is up now, including the revised version of the slides which fixes a few typos and rounds out references.
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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.)
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Introduction: Yann and I have arranged so that people who are interested in our large scale machine learning class and not able to attend in person can follow along via two methods. Videos will be posted with about a 1 day delay on techtalks . This is a side-by-side capture of video+slides from Weyond . We are experimenting with Piazza as a discussion forum. Anyone is welcome to subscribe to Piazza and ask questions there, where I will be monitoring things. update2 : Sign up here . The first lecture is up now, including the revised version of the slides which fixes a few typos and rounds out references.
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Introduction: The large scale machine learning class I taught with Yann LeCun has finished. As I expected, it took quite a bit of time . We had about 25 people attending in person on average and 400 regularly watching the recorded lectures which is substantially more sustained interest than I expected for an advanced ML class. We also had some fun with class projects—I’m hopeful that several will eventually turn into papers. I expect there are a number of professors interested in lecturing on this and related topics. Everyone will have their personal taste in subjects of course, but hopefully there will be some convergence to common course materials as well. To help with this, I am making the sources to my presentations available . Feel free to use/improve/embelish/ridicule/etc… in the pursuit of the perfect course.
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Introduction: This is a difficult subject to talk about for many reasons, but a discussion may be helpful. Bad reviewing is a problem in academia. The first step in understanding this is admitting to the problem, so here is a short list of examples of bad reviewing. Reviewer disbelieves theorem proof (ICML), or disbelieve theorem with a trivially false counterexample. (COLT) Reviewer internally swaps quantifiers in a theorem, concludes it has been done before and is trivial. (NIPS) Reviewer believes a technique will not work despite experimental validation. (COLT) Reviewers fail to notice flaw in theorem statement (CRYPTO). Reviewer erroneously claims that it has been done before (NIPS, SODA, JMLR)—(complete with references!) Reviewer inverts the message of a paper and concludes it says nothing important. (NIPS*2) Reviewer fails to distinguish between a DAG and a tree (SODA). Reviewer is enthusiastic about paper but clearly does not understand (ICML). Reviewer erroneously
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