This response on CrossValidated, a StackExchange board, by a Nobel Laureate did not strike me a necessarily awesome until I got to that sentence:
I'm in the process of writing a summary blog post to draw this together.
Mmmmuh, a Nobel Laureate thinks it is important to not just answer questions on a StackExchange board but that it is also very important to write a blog entry to provide a bigger picture. While I am not worried that his point of view will get some traffic, I am worried about filter bubbles. There are great applications like Prismatic that allows one to discover information through what others in your social network have seen. However I am very much worried that discovery, which is what this blog is about, could be hampered if we are not bursting in some consistent way these filter bubbles. If you have read the blog for a while, you'll know that serendipity has been essential for some blog entries and eventually for some papers.
Let me go further in this quest for bursting the filter of our peers. Currently there are a slew of sites that features random and interesting elements of knowledge. Let me take for instance two of them: Reddit and Metafilter. These two boards are specifically banning whoever posts about their own blogs or their own material. I personally don't know how to go about posting to these boards when there is something posted here that is truly amazing. I prefer posting here but then that automatically stops me from posting to these outlets. This blog's content is not really about my own production but really a way to put in context some of the most advanced research out there. It is in fact so much at the edge of what we know that nobody else besides a few specialists have really touched or even learned about it. Since most preprints do not see the light of the day in terms of publication until one or two years later, we cannot really afford good ideas and implementation languish in the peer review stage for ever. Since I have a bad experience with the Machine Learning forum on Reddit, I just decided to create a new subreddit called CompressiveSensing. If you are in the Reddit community, please join the group and post links or whatever you think fits into the very general themes of compressive sensing (in the most liberal use of the word, yes that includes machine learning) or advanced matrix factorization.
Let me go further in this quest for bursting the filter of our peers. Currently there are a slew of sites that features random and interesting elements of knowledge. Let me take for instance two of them: Reddit and Metafilter. These two boards are specifically banning whoever posts about their own blogs or their own material. I personally don't know how to go about posting to these boards when there is something posted here that is truly amazing. I prefer posting here but then that automatically stops me from posting to these outlets. This blog's content is not really about my own production but really a way to put in context some of the most advanced research out there. It is in fact so much at the edge of what we know that nobody else besides a few specialists have really touched or even learned about it. Since most preprints do not see the light of the day in terms of publication until one or two years later, we cannot really afford good ideas and implementation languish in the peer review stage for ever. Since I have a bad experience with the Machine Learning forum on Reddit, I just decided to create a new subreddit called CompressiveSensing. If you are in the Reddit community, please join the group and post links or whatever you think fits into the very general themes of compressive sensing (in the most liberal use of the word, yes that includes machine learning) or advanced matrix factorization.
In the meantime, some of you are helping your students' out by pointing to Nuit Blanche or the Rice CS page as a reference for Compressive Sensing information, here are the latest I have seen (please let me know if there are others than the ones listed here)
(h/t to Albert Vilella for pointing the info on CrossValidated )
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