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Room for Doubt

reflections on machine learning, computer science, and academia

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Room for Doubt | levreyzin.blogspot.com Reviews
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reflections on machine learning, computer science, and academia
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Room for Doubt | levreyzin.blogspot.com Reviews

https://levreyzin.blogspot.com

reflections on machine learning, computer science, and academia

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Room for Doubt: UIC's MCS Program Seeking Graduate Students

http://levreyzin.blogspot.com/2012/12/uics-mcs-program-seeking-graduate.html

Reflections on machine learning, computer science, and academia. Friday, December 07, 2012. UIC's MCS Program Seeking Graduate Students. As I'd posted before, UIC has two departments interested in computer science. We have a computer science department. In the engineering school. And the math department. In the school of liberal arts and sciences, where I have an appointment, has a smaller program in "mathematical computer science" (MCS). We recently updated our website. Posted by Lev Reyzin.

2

Room for Doubt: Elementary, My Dear Watson?

http://levreyzin.blogspot.com/2011/02/elementary-my-dear-watson.html

Reflections on machine learning, computer science, and academia. Tuesday, February 15, 2011. Elementary, My Dear Watson? As many of you know, this week IBM. S computer system, Watson. Ever since witnessing humanity's line fall when Deep Blue. But few of us will be surprised when, in a couple years, this ceases to be the case. Observes on twitter, the final Jeopardy question. Computer systems - only they'll come much before the 24th century. Don't get me wrong; there's still quite a bit to go. Rea...Final...

3

Room for Doubt: Atlanta, Northward

http://levreyzin.blogspot.com/2012/05/atlanta-northward.html

Reflections on machine learning, computer science, and academia. Monday, May 07, 2012. When you accept a postdoctoral position, you know you probably can't stay indefinitely. Psychologically, it means you have to prepare yourself for having to switch jobs, coworkers, offices, etc., and I, as a postdoc, have been ready for this foreseeable consequence with Georgia Tech. Where I've enjoyed being part of a strong and lively department the last two years. Where there isn't as high a concentration. In my care...

4

Room for Doubt: Diamond Forever

http://levreyzin.blogspot.com/2012/08/diamond-forever.html

Reflections on machine learning, computer science, and academia. Tuesday, August 07, 2012. Recently, the academic community started to fight back. Against traditional academic publishers, who earn large profits without any longer providing many clear services. The internet has been a great tool for taking care of such inefficiencies, and it's quickly disrupting the publishing market as well. Screenshot, after I tried to access a paper from nature.com. Publishers, by charging for access. This system made ...

5

Room for Doubt: 2014 in Review

http://levreyzin.blogspot.com/2014/12/2014-in-review.html

Reflections on machine learning, computer science, and academia. Wednesday, December 31, 2014. As 2014 comes to an end, I decided I want to continue my newfound tradition. Of summarizing my thoughts in a "year in review" post. So here are some thoughts on academia, machine learning, and theory, again in no particular order. Every company seems to have its. Leading a neural nets effort. And deep learning keeps making impressive advances. My hope that a nicer learning theory gets developed around this topic.

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Musings about Machine Learning, Technology and Teaching: September 2009

http://readingsml.blogspot.com/2009_09_01_archive.html

Musings about Machine Learning, Technology and Teaching. Monday, 7 September 2009. How to make Thunderbird delete temporary file. If you are using Thunderbird. TB) on Mac OSX. You might be annoyed by that when TB opens an attachment (like a pdf file) it creates the file on the Desktop and then leaves just it there! I have finally found a solution, which seems to work at least for me and assuming that you also have Firefox. The solution is here. But I duplicate it here to make sure the idea spreads:.

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Musings about Machine Learning, Technology and Teaching: Brains, Minds and Machines

http://readingsml.blogspot.com/2011/05/brains-minds-and-machines.html

Musings about Machine Learning, Technology and Teaching. Wednesday, 4 May 2011. Brains, Minds and Machines. Former UofA student, Alborz. To a video recording of a recent MIT150 symposium on Brains, Minds and Machines on facebook. I watched the video yesterday (guess what, I need to mark 40 something finals, hehe:) . I wrote a comment back to Alborz on facebook and then I thought, why not make this a blog post? 8232;" and the panelist were Emilio Bizzi,. Barbara H. Partee and. Patrick H. Winston. A big sh...

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Musings about Machine Learning, Technology and Teaching: July 2007

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Musings about Machine Learning, Technology and Teaching. Monday, 30 July 2007. It is quite a common sense that successful generalization is the key to efficient learning in difficult environment. It appears to me that this must be especially true for reinforcement learning. One potentially very powerful idea to achieve successful generalization is to learn symbolic models. Why? However, one thing is badly missing from this approach: learning the representation itself. It appears that the authors' ass...

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Musings about Machine Learning, Technology and Teaching: Approximating inverse covariance matrices

http://readingsml.blogspot.com/2012/04/approximating-inverse-covariance.html

Musings about Machine Learning, Technology and Teaching. Saturday, 14 April 2012. Approximating inverse covariance matrices. Phew, the last time I have posted an entry to my blog was a loong time ago. Not that there was nothing interesting to blog about, just I always delayed things. (Btw, google changed the template which eliminated the rendering of the latex formulae, not happy. Luckily, I could change back the template.) Now, as the actual contents:. In short, the author's point is this:. Anyhow, one ...

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Musings about Machine Learning, Technology and Teaching: October 2009

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Musings about Machine Learning, Technology and Teaching. Saturday, 31 October 2009. This is a little deviation from the usual topic. The motivation for the research is to cure diseases like Parkinson's disease, when the patient has all the circuity and muscles but is just unable to make the movements. In fact, the researchers are already testing this technology on primates. Source: Wired Nov. 2009, "Powered by Photons" pp. 109- 113. The wikipedia entry for optogenetics is here. Posted by Csaba Szepesvari.

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Musings about Machine Learning, Technology and Teaching: April 2008

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Musings about Machine Learning, Technology and Teaching. Saturday, 5 April 2008. See more crazy cat pics. Posted by Csaba Szepesvari. Links to this post. Subscribe to: Posts (Atom). This blog uses MathML. To read it you should have a MathML enabled browser. Internet Explorer 6 requires MathPlayer. While MathML should work seamlessly with Netscape7/Mozilla/Firefox if Javascript is enabled. Many thanks for Peter Jipsen. And the folks who developed ASCIIMathML. Which serves as the basis of LaTeXMathML.

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Musings about Machine Learning, Technology and Teaching: April 2012

http://readingsml.blogspot.com/2012_04_01_archive.html

Musings about Machine Learning, Technology and Teaching. Saturday, 14 April 2012. Approximating inverse covariance matrices. Phew, the last time I have posted an entry to my blog was a loong time ago. Not that there was nothing interesting to blog about, just I always delayed things. (Btw, google changed the template which eliminated the rendering of the latex formulae, not happy. Luckily, I could change back the template.) Now, as the actual contents:. In short, the author's point is this:. Anyhow, one ...

readingsml.blogspot.com readingsml.blogspot.com

Musings about Machine Learning, Technology and Teaching: Numerical Errors, Perturbation Analysis and Machine Learning

http://readingsml.blogspot.com/2013/09/numerical-errors-perturbation-analysis.html

Musings about Machine Learning, Technology and Teaching. Monday, 16 September 2013. Numerical Errors, Perturbation Analysis and Machine Learning. This issue should be well understood. However, I don't think that it is as well appreciated as it should be, or that it received the attention it should. In fact, I doubt that the issue is discussed in any of the recent machine learning textbooks beyond the usual caveat "beware the numerical errors" (scary! P(f) = arg min {g in G} f-g 2$, (Proj). A theta = b$.

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Musings about Machine Learning, Technology and Teaching: August 2007

http://readingsml.blogspot.com/2007_08_01_archive.html

Musings about Machine Learning, Technology and Teaching. Sunday, 12 August 2007. Discriminative vs. generative learning: which one is more efficient? I just came across a paper by Philip M. Long. And Hans Ulrich Simon. Is a link to the paper titled "Discriminative Learning can Succeed where Generative Learning Fails".) The question investigated in the paper is the following:. We are in a classification setting and the learning problem is defined by a pair of jointly distributed random variables, (X,Y).

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Room for Doubt

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