hunch_net hunch_net-2008 hunch_net-2008-296 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: I found the article about science using modern tools interesting , especially the part about ‘blogophobia’, which in my experience is often a substantial issue: many potential guest posters aren’t quite ready, because of the fear of a permanent public mistake, because it is particularly hard to write about the unknown (the essence of research), and because the system for public credit doesn’t yet really handle blog posts. So far, science has been relatively resistant to discussing research on blogs. Some things need to change to get there. Public tolerance of the occasional mistake is essential, as is a willingness to cite (and credit) blogs as freely as papers. I’ve often run into another reason for holding back myself: I don’t want to overtalk my own research. Nevertheless, I’m slowly changing to the opinion that I’m holding back too much: the real power of a blog in research is that it can be used to confer with many people, and that just makes research work better.
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4 Nevertheless, I’m slowly changing to the opinion that I’m holding back too much: the real power of a blog in research is that it can be used to confer with many people, and that just makes research work better. [sent-6, score-1.57]
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Introduction: I found the article about science using modern tools interesting , especially the part about ‘blogophobia’, which in my experience is often a substantial issue: many potential guest posters aren’t quite ready, because of the fear of a permanent public mistake, because it is particularly hard to write about the unknown (the essence of research), and because the system for public credit doesn’t yet really handle blog posts. So far, science has been relatively resistant to discussing research on blogs. Some things need to change to get there. Public tolerance of the occasional mistake is essential, as is a willingness to cite (and credit) blogs as freely as papers. I’ve often run into another reason for holding back myself: I don’t want to overtalk my own research. Nevertheless, I’m slowly changing to the opinion that I’m holding back too much: the real power of a blog in research is that it can be used to confer with many people, and that just makes research work better.
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same-blog 1 0.95355076 296 hunch net-2008-04-21-The Science 2.0 article
Introduction: I found the article about science using modern tools interesting , especially the part about ‘blogophobia’, which in my experience is often a substantial issue: many potential guest posters aren’t quite ready, because of the fear of a permanent public mistake, because it is particularly hard to write about the unknown (the essence of research), and because the system for public credit doesn’t yet really handle blog posts. So far, science has been relatively resistant to discussing research on blogs. Some things need to change to get there. Public tolerance of the occasional mistake is essential, as is a willingness to cite (and credit) blogs as freely as papers. I’ve often run into another reason for holding back myself: I don’t want to overtalk my own research. Nevertheless, I’m slowly changing to the opinion that I’m holding back too much: the real power of a blog in research is that it can be used to confer with many people, and that just makes research work better.
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Introduction: It was a fine time for learning in Pittsburgh. John and Sam mentioned some of my favorites. Here’s a few more worth checking out: Online Multitask Learning Ofer Dekel, Phil Long, Yoram Singer This is on my reading list. Definitely an area I’m interested in. Maximum Entropy Distribution Estimation with Generalized Regularization Miroslav DudÃÂk, Robert E. Schapire Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path András Antos, Csaba Szepesvári, Rémi Munos Again, on the list to read. I saw Csaba and Remi talk about this and related work at an ICML Workshop on Kernel Reinforcement Learning. The big question in my head is how this compares/contrasts with existing work in reductions to reinforcement learning. Are there advantages/disadvantages? Higher Order Learning On Graphs> by Sameer Agarwal, Kristin Branson, and Serge Belongie, looks to be interesteding. They seem to poo-poo “tensorization
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