Recent submissions

Comment · 2 years ago
Comment · 3 years ago
think the author is talking about having to fix the extra vector in V to be zeros and making sure to not compute/apply gradients to it
Comment · 5 years ago
I think you are going to confuse people by framing this as "time series" and then focussing on reinforcement learning. A lot/most of your framing is around RL.

from a first glance and a read through your roadmap, this does not feel suitable for people who know what they are doing with RL. It also does not feel suitable for people who don't know what they are doing with RL.

Comment · 5 years ago
Eh I feel like may be worth cutting some slack here..

It sounds like you’d like the title to be prefaced with “one time that”.

I don’t think anyone reading this says, hey gans are done for. I doubt the authors think that and I (clearly) don’t get the impression that’s what they mean.

Even the first sentence of the abstract:

> We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models

Has the word current in front of state of the art generative models.

Comment · 5 years ago
Comment · 6 years ago
many scientific computing applications are considered to be bounded by io
Comment · 6 years ago
Comment · 6 years ago
Do you have any evidence that they are lying about all of the use cases they list on that web site?
Comment · 6 years ago
Comment · 6 years ago
actually, I think this is exactly what misappropriation theory is supposed to account for. can you explain more around why it is well within their rights? I don't think it is.
Comment · 6 years ago
Svms are, by default, linear models. The decision boundary in the Svm problem is linear and since it’s the max margin we may enjoy nice generalization properties (as you probably know).

You probably also know that decision tree boundaries are non Linear And piecewise. It’s not so straightforward to find splits on continuous features.

Ie If the data is linearly separable then why not. Even using hinge loss with nns is not uncommon.

You probably see gbms winning a lot of competitions compared to svms because a lot of competitions may have a lot of data and non linear decision boundaries. some problems don’t have these characteristics.

Comment · 6 years ago
Comment · 6 years ago
in NYC una is the best i've found. it easily competes with the best in naples (in my opinion)
Comment · 6 years ago
Thanks, that is pretty impressive

Live from the official Hacker News API

A modern reader for Hacker News · SvelteKit + Skeleton