There has been a lot of stuff going on recently and I've been super busy. I have a few posts in early stage of development and a few ideas in the pipeline but it will likely take me quite some time before I get this stuff to a state in which it would be readable.
In the meanwhile, by a complete coincidence I've learned that my 2017 PVM talk I gave a University of California Merced is actually available online. It was a very good visit, organised by Chris Kello, David Noelle and others. I had some good chats with these guys and with Jeff Yoshimi (author of simbrain) among others. Somehow I did not realize the talk was recorded... Anyway, here it is, better late than never I guess. Since I generally hate to listen to myself, I had to increase the playback speed to 2.0 at which point it actually sounded OK, so I recommend those settings (plus it only takes 50% of the time).
Slides are available here.
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I read a lot of deep learning papers, typically a few/week. I've read probably several thousands of papers. My general problem with papers in machine learning or deep learning is that often they sit in some strange no man's land between science and engineering, I call it "academic engineering". Let me describe what I mean:
- A scientific paper IMHO, should convey an idea that has the ability to explain something. For example a paper that proves a mathematical theorem, a paper that presents a model of some physical phenomenon. Alternatively a scientific paper could be experimental, where the result of an experiment tells us something fundamental about the reality. Nevertheless the central point of a scientific paper is a relatively concisely expressible idea of some nontrivial universality (and predictive power) or some nontrivial observation about the nature of reality.
- An engineering paper shows a method of solving a particular problem. Problems may vary and depend on an application, sometimes they could be really uninteresting and specific but nevertheless useful for somebody somewhere. For an engineering paper, things that matter are different than for a scientific paper: the universality of the solution may not be of paramount importance. What matters
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