"How often should you beat your kids?", by Don Zagier.
> This note is a follow-up to the note "How to beat your kids at their own game" by K. Levasseur, in which the author proposes the following game to be played against one's own children: ... Levasseur analyzes the game and shows that on average you will have a score of n + (sqrt(pi * n) - 1) / 2 + O(n^{-1/2}), while the kid, of course, will have an average score of exactly n.
> We maintain, however, that only the most degenerate parent would play against a 2-year-old for money, and that our concern should therefore be not by how much you expect to win, but with probability you will win at all.
I really want people to crowdsource the DMT prime factorisation project. I know at least one person tried but lost interest before they met an elf. It just seems like such a fun experiment to run. Is it possible to recall numbers at all while taking DMT? Can you memorise new ones? If not why not etc, and maybe the machine God factorises numbers for you!
No. I think the need for adversarial losses in order to distill diffusion models into one-step forward passes has provided additional evidence that GANs were much more viable than diffusimaxis loudly insisted.
(Although I'm not really current on where image generation is these days or who is using GAN-like approaches under the hood or what are the current theoretical understandings of GAN vs AR vs diffusion, so if you have some specific reason I should have "caved", feel free to mention it - I may well just be unaware of it.)
"SotA diffusion uses adversarial methods anyways" seems like a bit of a departure from the case you make in the blog post.
edit: For what it's worth - I agree. At least some auto-encoders (which will produce latents for diffusion models) use some form of adversarial method.
Still, I'm curious if you think GAN models in their more familiar form are going to eventually take on LCM/diffusion models?
https://people.mpim-bonn.mpg.de/zagier/files/math-mag/63-2/f...
"How often should you beat your kids?", by Don Zagier.
> This note is a follow-up to the note "How to beat your kids at their own game" by K. Levasseur, in which the author proposes the following game to be played against one's own children: ... Levasseur analyzes the game and shows that on average you will have a score of n + (sqrt(pi * n) - 1) / 2 + O(n^{-1/2}), while the kid, of course, will have an average score of exactly n.
> We maintain, however, that only the most degenerate parent would play against a 2-year-old for money, and that our concern should therefore be not by how much you expect to win, but with probability you will win at all.
I really want people to crowdsource the DMT prime factorisation project. I know at least one person tried but lost interest before they met an elf. It just seems like such a fun experiment to run. Is it possible to recall numbers at all while taking DMT? Can you memorise new ones? If not why not etc, and maybe the machine God factorises numbers for you!
Not directly related, but I'm curious if gwern ever caved on their views about GAN's being "abandoned" for diffusion?
https://gwern.net/gan
No. I think the need for adversarial losses in order to distill diffusion models into one-step forward passes has provided additional evidence that GANs were much more viable than diffusimaxis loudly insisted.
(Although I'm not really current on where image generation is these days or who is using GAN-like approaches under the hood or what are the current theoretical understandings of GAN vs AR vs diffusion, so if you have some specific reason I should have "caved", feel free to mention it - I may well just be unaware of it.)
"SotA diffusion uses adversarial methods anyways" seems like a bit of a departure from the case you make in the blog post.
edit: For what it's worth - I agree. At least some auto-encoders (which will produce latents for diffusion models) use some form of adversarial method.
Still, I'm curious if you think GAN models in their more familiar form are going to eventually take on LCM/diffusion models?