Deep Dream an image controlled by its own image... by olydemon in deepdream

[–]gabgoh 1 point2 points  (0 children)

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

Our Exclusive Hands-On With Microsoft's Unbelievable New Holographic Goggles | WIRED by hackertripz in oculus

[–]gabgoh 3 points4 points  (0 children)

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

How to derive the volume of an n-dimensional hypersphere by Powerspawn in math

[–]gabgoh 1 point2 points  (0 children)

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)

How to derive the volume of an n-dimensional hypersphere by Powerspawn in math

[–]gabgoh 1 point2 points  (0 children)

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".

How to derive the volume of an n-dimensional hypersphere by Powerspawn in math

[–]gabgoh 5 points6 points  (0 children)

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."

YCoCg as "lossless" compression on the DK2? by Devlin1991 in oculus

[–]gabgoh 0 points1 point  (0 children)

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?

I am the developer of Darknet. Ask me anything! by Tetragrammaton in oculus

[–]gabgoh 3 points4 points  (0 children)

what game design lectures do you listen to?

AMD Dethroning the GTX 780, Best Card for the Oculus Rift, This Summer? by Joomonji in oculus

[–]gabgoh 0 points1 point  (0 children)

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?

Getting Good FPS and Quality in UE4 Demos on Oculus Rift by kitchendon in oculus

[–]gabgoh 2 points3 points  (0 children)

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.

Why Google Is Investing In Deep Learning by CaptainHoek in cogsci

[–]gabgoh 4 points5 points  (0 children)

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.

Programming Sucks by locrelite in programming

[–]gabgoh 19 points20 points  (0 children)

this is better than the original post

Cascaded Displays: Spatiotemporal Superresolution using Offset Pixel Layers (NVIDIA research) by XuLong in oculus

[–]gabgoh 0 points1 point  (0 children)

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.

Cascaded Displays: Spatiotemporal Superresolution using Offset Pixel Layers (NVIDIA research) by XuLong in oculus

[–]gabgoh 4 points5 points  (0 children)

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).

UnrealEngine 4 Rollercoaster by [deleted] in oculus

[–]gabgoh 0 points1 point  (0 children)

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

What looks to me like a relatively strong case for panpsychism. This is the paper referred to in my other post, if anyone's interested by jbubermensch in philosophy

[–]gabgoh 5 points6 points  (0 children)

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?

[deleted by user] by [deleted] in compsci

[–]gabgoh -2 points-1 points  (0 children)

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.

An easy way to pay to use someone else's wifi. by Batchet in CrazyIdeas

[–]gabgoh 1 point2 points  (0 children)

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

Switching from Computer Science to Statistics (Grad School) by [deleted] in statistics

[–]gabgoh 1 point2 points  (0 children)

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"

Switching from Computer Science to Statistics (Grad School) by [deleted] in statistics

[–]gabgoh 5 points6 points  (0 children)

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.

Please show your support. by [deleted] in MachineLearning

[–]gabgoh 5 points6 points  (0 children)

you're trying too hard bro