hunch_net hunch_net-2005 hunch_net-2005-24 knowledge-graph by maker-knowledge-mining
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Introduction: Yaroslav collected an extensive list of machine learning reading groups .
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same-blog 1 1.0 24 hunch net-2005-02-19-Machine learning reading groups
Introduction: Yaroslav collected an extensive list of machine learning reading groups .
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Introduction: Yaroslav Bulatov collects some links to other technical blogs.
3 0.083916813 278 hunch net-2007-12-17-New Machine Learning mailing list
Introduction: IMLS (which is the nonprofit running ICML) has setup a new mailing list for Machine Learning News . The list address is ML-news@googlegroups.com, and signup requires a google account (which you can create). Only members can send messages.
4 0.066110864 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
5 0.060634878 50 hunch net-2005-04-01-Basic computer science research takes a hit
Introduction: The New York Times has an interesting article about how DARPA has dropped funding for computer science to universities by about a factor of 2 over the last 5 years and become less directed towards basic research. Partially in response, the number of grant submissions to NSF has grown by a factor of 3 (with the NSF budget staying approximately constant in the interim). This is the sort of policy decision which may make sense for the defense department, but which means a large hit for basic research on information technology development in the US. For example “darpa funded the invention of the internet” is reasonably correct. This policy decision is particularly painful in the context of NSF budget cuts and the end of extensive phone monopoly funded research at Bell labs. The good news from a learning perspective is that (based on anecdotal evidence) much of the remaining funding is aimed at learning and learning-related fields. Methods of making good automated predictions obv
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same-blog 1 0.90323293 24 hunch net-2005-02-19-Machine learning reading groups
Introduction: Yaroslav collected an extensive list of machine learning reading groups .
2 0.65335023 15 hunch net-2005-02-08-Some Links
Introduction: Yaroslav Bulatov collects some links to other technical blogs.
3 0.64411795 278 hunch net-2007-12-17-New Machine Learning mailing list
Introduction: IMLS (which is the nonprofit running ICML) has setup a new mailing list for Machine Learning News . The list address is ML-news@googlegroups.com, and signup requires a google account (which you can create). Only members can send messages.
4 0.57876384 342 hunch net-2009-02-16-KDNuggets
Introduction: Eric Zaetsch points out KDNuggets which is a well-developed mailing list/news site with a KDD flavor. This might particularly interest people looking for industrial jobs in machine learning, as the mailing list has many such.
5 0.42288595 428 hunch net-2011-03-27-Vowpal Wabbit, v5.1
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: Yaroslav collected an extensive list of machine learning reading groups .
2 0.81445467 421 hunch net-2011-01-03-Herman Goldstine 2011
Introduction: Vikas points out the Herman Goldstine Fellowship at IBM . I was a Herman Goldstine Fellow, and benefited from the experience a great deal—that’s where work on learning reductions started. If you can do research independently, it’s recommended. Applications are due January 6.
3 0.63799077 482 hunch net-2013-05-04-COLT and ICML registration
Introduction: Sebastien Bubeck points out COLT registration with a May 13 early registration deadline. The local organizers have done an admirable job of containing costs with a $300 registration fee. ICML registration is also available, at about an x3 higher cost. My understanding is that this is partly due to the costs of a larger conference being harder to contain, partly due to ICML lasting twice as long with tutorials and workshops, and partly because the conference organizers were a bit over-conservative in various ways.
4 0.45194098 438 hunch net-2011-07-11-Interesting Neural Network Papers at ICML 2011
Introduction: Maybe it’s too early to call, but with four separate Neural Network sessions at this year’s ICML , it looks like Neural Networks are making a comeback. Here are my highlights of these sessions. In general, my feeling is that these papers both demystify deep learning and show its broader applicability. The first observation I made is that the once disreputable “Neural” nomenclature is being used again in lieu of “deep learning”. Maybe it’s because Adam Coates et al. showed that single layer networks can work surprisingly well. An Analysis of Single-Layer Networks in Unsupervised Feature Learning , Adam Coates , Honglak Lee , Andrew Y. Ng (AISTATS 2011) The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization , Adam Coates , Andrew Y. Ng (ICML 2011) Another surprising result out of Andrew Ng’s group comes from Andrew Saxe et al. who show that certain convolutional pooling architectures can obtain close to state-of-the-art pe
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Introduction: There were two papers at ICML presenting learning algorithms for a contextual bandit -style setting, where the loss for all labels is not known, but the loss for one label is known. (The first might require a exploration scavenging viewpoint to understand if the experimental assignment was nonrandom.) I strongly approve of these papers and further work in this setting and its variants, because I expect it to become more important than supervised learning. As a quick review, we are thinking about situations where repeatedly: The world reveals feature values (aka context information). A policy chooses an action. The world provides a reward. Sometimes this is done in an online fashion where the policy can change based on immediate feedback and sometimes it’s done in a batch setting where many samples are collected before the policy can change. If you haven’t spent time thinking about the setting, you might want to because there are many natural applications. I’m g
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