Recent submissions

Comment · 6 years ago
Comment · 7 years ago
Comment · 7 years ago
There are many interesting ideas about how to come up with a "fair" online dating market.

But I guess everyone is completely disregarding the basics of human nature, attraction is mostly based on "looks/facial aesthetics":

https://www.reddit.com/r/BlackPillScience/

In a free/unregulated sexual market, women are the selectors, not men. Women are biologically hardwired to select good looking males (irrespective of his money, personality or social status). Therefore ugly people don't get matches. Very simple.

No matter how many ways you try to engineer/fix the system, it's not going to work. Because the existing system is already doing what it is supposed to do.

Comment · 7 years ago
I am going to hit 37 this year. I am still a student doing a PhD (an international student). These kind of story scares me.
Comment · 7 years ago
Comment · 7 years ago
Why do all those thinkpad bios logos feature anime girls? Also I am seeing one with that "Hitler and gas" meme. LMAO.
Comment · 7 years ago
I am actually quite surprised to see this kind of article on hn. Well, I am even more surprised because it's getting upvotes.

Please get a copy of Russel-Norvig's A.I. text and read at least some part of it before clicking that link.

Comment · 8 years ago
Comment · 8 years ago
Comment · 8 years ago
Why all these sightings are never accompanied with sonic booms? If something is moving that fast through the atmosphere, there must be a sonic boom. Or is there any way to suppress this? At least theoretically?
Comment · 8 years ago
Yes, that's true. But in optimization domain the concept of "convexity" is understood in terms of set, not always from the 2nd derivative of a function. Because you might have search spaces where you are not able to differentiate the objective function at all. In those cases the "convex" means a "convex set".
Comment · 8 years ago
>Just because you can prove convergence for convex functions does not mean you can't prove it for non-convex functions.

I don't understand. How do you prove a gradient descent is guaranteed to escape local minima?

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