hunch_net hunch_net-2005 hunch_net-2005-13 knowledge-graph by maker-knowledge-mining
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Introduction: The Journal of Machine Learning Gossip has some fine satire about learning research. In particular, the guides are amusing and remarkably true. As in all things, it’s easy to criticize the way things are and harder to make them better.
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same-blog 1 1.0 13 hunch net-2005-02-04-JMLG
Introduction: The Journal of Machine Learning Gossip has some fine satire about learning research. In particular, the guides are amusing and remarkably true. As in all things, it’s easy to criticize the way things are and harder to make them better.
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Introduction: Yesterday, there was a discussion about future publication models at NIPS . Yann and Zoubin have specific detailed proposals which I’ll add links to when I get them ( Yann’s proposal and Zoubin’s proposal ). What struck me about the discussion is that there are many simultaneous concerns as well as many simultaneous proposals, which makes it difficult to keep all the distinctions straight in a verbal conversation. It also seemed like people were serious enough about this that we may see some real movement. Certainly, my personal experience motivates that as I’ve posted many times about the substantial flaws in our review process, including some very poor personal experiences. Concerns include the following: (Several) Reviewers are overloaded, boosting the noise in decision making. ( Yann ) A new system should run with as little built-in delay and friction to the process of research as possible. ( Hanna Wallach (updated)) Double-blind review is particularly impor
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Introduction: From game theory, there is a notion of “mechanism design”: setting up the structure of the world so that participants have some incentive to do sane things (rather than obviously counterproductive things). Application of this principle to academic research may be fruitful. What is misdesigned about academic research? The JMLG guides give many hints. The common nature of bad reviewing also suggests the system isn’t working optimally. There are many ways to experimentally “cheat” in machine learning . Funding Prisoner’s Delimma. Good researchers often write grant proposals for funding rather than doing research. Since the pool of grant money is finite, this means that grant proposals are often rejected, implying that more must be written. This is essentially a “prisoner’s delimma”: anyone not writing grant proposals loses, but the entire process of doing research is slowed by distraction. If everyone wrote 1/2 as many grant proposals, roughly the same distribution
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Introduction: Here are some papers that I found surprisingly interesting. Yoshua Bengio , Pascal Lamblin, Dan Popovici, Hugo Larochelle, Greedy Layer-wise Training of Deep Networks . Empirically investigates some of the design choices behind deep belief networks. Long Zhu , Yuanhao Chen, Alan Yuille Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing. An unsupervised method for detecting objects using simple feature filters that works remarkably well on the (supervised) caltech-101 dataset . Shai Ben-David , John Blitzer , Koby Crammer , and Fernando Pereira , Analysis of Representations for Domain Adaptation . This is the first analysis I’ve seen of learning with respect to samples drawn differently from the evaluation distribution which depends on reasonable measurable quantities. All of these papers turn out to have a common theme—the power of unlabeled data to do generically useful things.
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same-blog 1 0.80551505 13 hunch net-2005-02-04-JMLG
Introduction: The Journal of Machine Learning Gossip has some fine satire about learning research. In particular, the guides are amusing and remarkably true. As in all things, it’s easy to criticize the way things are and harder to make them better.
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same-blog 1 0.75432014 13 hunch net-2005-02-04-JMLG
Introduction: The Journal of Machine Learning Gossip has some fine satire about learning research. In particular, the guides are amusing and remarkably true. As in all things, it’s easy to criticize the way things are and harder to make them better.
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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.
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