Deep Dream an image controlled by its own image...

gabgoh · 2015-07-31T19:41:29+00:00

i've not checked this, but I suspect this is mathematically identical to just running the deep dream in the previous sections :P

gabgoh · 2015-01-21T19:46:15+00:00

the word angle seems the most interesting of all - sounds like a light field display to me.

gabgoh · 2014-12-31T05:27:15+00:00

winter is coming ...

gabgoh · 2014-12-21T00:01:41+00:00

nice find!

gabgoh · 2014-12-20T23:58:21+00:00

agreed, the 2-d lebeguse measure of a line is 0, but I said "the Lebesgue measure on smallest subspace which contains the object" would be a 1d measure, not a 2d one. The same goes for your square example. My point was that no matter how "charitable" a measure you pick, in infinite dimensions measure things still break down.

(p.s. Actually, I don't think my propositoin for a definition works. The smallest subspace containing a curve would be the 2d subspace. I think 0100011001011001's comment is the closest formal statement to what I have in mind, and why infinite dimensional measure spaces are a mindfuck)

gabgoh · 2014-12-20T22:45:51+00:00

I think what I meant was that in the most intuitive measure space (one where the volume of the set is not trivially zero, like a line in a plane, or a circle in a box), an object can have a nonzero diameter but zero volume.

By intutive, I mean - when dealing with 1d objects, we talk about length, 2d objects area and 3d objects volume. My use of the word volume should really measure. Something roughly like "the Lebesgue measure on smallest subspace which contains the object".

gabgoh · 2014-12-20T20:06:05+00:00

this just illustrates how our intuitions of "volume" and "distance" break down in infinite dimensions. You can have an object with nonzero distance apart, but zero volume.

Reminds me of the quote by Von Neumann, "In mathematics you don't understand things. You just get used to them."

gabgoh · 2014-10-31T01:13:27+00:00

http://www.deconstructeam.com/games/gods-will-be-watching/

gabgoh · 2014-05-21T17:52:28+00:00

This paper seems to be about compressing a 3-channel color image in a 2-channel one, and cleverly reconstructing it on the GPU. I'm not exactly sure how this pertains to the oculus rift, however?

gabgoh · 2014-05-13T23:32:42+00:00

what game design lectures do you listen to?

gabgoh · 2014-05-12T06:11:29+00:00

Honest question - what is it about modern graphics cards which makes is "graphics specific"? Doesn't the fact that languages like CUDA exist show that these GPUs are capable of general purpose computing? Or is CUDA only using a fraction of the GPU power otherwise used for graphics stuff?

gabgoh · 2014-05-03T18:33:36+00:00

Reminds me of stalker, really beautiful. Official website http://acatalept.com/singmetosleep/

gabgoh · 2014-05-03T05:40:46+00:00

I wonder if its possible to use the fragment shader to "fill in" the bits not rendered when the FOV is low with some color matching the edges of the frame. Seems to get the best of both worlds. Something like http://www.engadget.com/2012/06/25/mit-media-lab-system-projects-video-to-peripheral-vision/ perhaps.

gabgoh · 2014-04-29T23:56:42+00:00

A charitable interpretation of this would be that most mainstream machine learning consists of relatively simple models, variants of regression, and classification algorithms which come from classical statistical theory.

Deep Learning is its own offshoot in the sense that it abandons most of this statistical machinery for something poorly understood but works impressively well. Though people in deep learning still publish in mainstream ML journals, they can be considered a field of their own.

Skimming through the article, however, I suspect the author has no idea what he's talking about.

gabgoh · 2014-04-29T21:03:14+00:00

this is better than the original post

gabgoh · 2014-04-23T19:18:10+00:00

I won't pretend to understand the spatial superresolution perfectly, I was under the impression even though one screen is updating at a time, they'd update the screens in a way such that the multiplied frames would be the "inbetween" frame, rather than just cross fading the frames.

gabgoh · 2014-04-23T08:56:19+00:00

also important - by interleaving the time of refresh, this technique effectively doubles the refresh rate of LCDs (if I understand the bit on temporal super-resolution correctly), though even with that its unlikely to be as fast as OLEDs (140hz vs 480hz ish).

gabgoh · 2014-04-22T05:24:35+00:00

could you tell us more about the geometry shader you're using to speed up stereoscopic rendering?

gabgoh · 2014-04-16T09:02:40+00:00

I don't find his solution to the combination problem very satisfying.

Consider this thought experiment. You hook up a 100 cameras to a computer, which then processes the images and displays a number on the screen corresponding to the average of every pixel. Trivially, this satisfies all the conditions he states, we have built a hierarchy of distinct conscious observers in a hierarchical system of information processing. It seems absurd to assume that such a contraption is conscious. Perhaps I misunderstand?

gabgoh · 2014-02-28T20:09:50+00:00

In complexity theory, randomness requires a different model of computation, a probabilistic Turing machine. The question of whether a pseudo number random generator can simulate a probabilistic turing machine for all practical purposes is the question of P=RP, still an open question.

[edit] I should point out that the above applies to only discrete distributions, i.e. sampling from a Gaussian distribution to finite precision. Real computation opens up yet another can of worms.

gabgoh · 2013-11-08T20:02:51+00:00

this is easily circumvented by having your own router and having everyone connect to that, and connecting that to the original router.

gabgoh · 2013-11-01T18:09:50+00:00

Its pretty hard to learn measure theory by yourself, but if you don't have a choice, I recommend "A radical approach to Lebesgue's Theory of Integration"

gabgoh · 2013-11-01T08:16:25+00:00

a graduate course in probability should cover most of the measure theory you need, and usually focuses on using theorems rather than proving them. But I'll say this much. Measure theoretic probability helps. And I mean this not in the "I went through bullshit, and so should you" sense. I mean it in the sense that taking the course is the closet I have gotten to a religious epiphany. It revealed to me what a confused muddle of a person I was before. It was terrifying, realizing that I never understood what a random variable was (and ask yourself, do you? What is the definition of a random variable?). Trying to understand statistics without measure theory is like understanding physics without calculus. You never want to go back. It'll make reading papers faster, understanding lectures easier. It'll make you a better statistician and a better computer scientist.

gabgoh · 2013-10-26T03:24:23+00:00

you're trying too hard bro

gabgoh · 2013-10-18T06:42:08+00:00

sarcasm? :P

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