andrew3726

It's Noam Brown, he worked at Meta AI before on Cicero and No-hands Poker before that.

Sure, that's called audio super resolution, there's a few papers/projects doing that. Haven't really seen models which are robust and have good generalization though.

They have a section on their Website about the AV issues.

https://blog.nirsoft.net/category/antivirus-issues/

This is also very important in many global illumination techniques which requires sampling the hemisphere around a point. As case in point a simple Monte Carlo path tracer uses this to sample random directions, see e.g. [0]. In addition to this there are many 'tricks' one can use to reduce the variance/noise using a differently weighted sampling (to help the converge of MC). A nice technique to get a cosine-weighted hemisphere sampling is to generate a random point on the unit disc and then project them onto the (hemi)sphere. Seems somewhat relevant here.

[0] https://en.wikipedia.org/wiki/Path_tracing#Algorithm

That definitely sounds like some predisposition. The whole idea around intermittent fasting revolves around eating in a specific time window, outside of that you don't consume any calories at all. Many people I know of do this, in order to lose some weight or gain muscles without adding too much fat, never heard of this problem.

Why would human versatility define the upper limit?

Well, that's been fixed with Wayland, right?

UI of the browser.

Well, for me at least, I always recognize if a button click/UI interaction doesn't force a visible and immediate UI response. That's one of the main reasons why I switched a couple of years to Chromium and now back to Firefox. It really responds fast. I totally agree that there were _very_ few instances where I noticed a rendering time difference.

Not on a pc right now, but there may be an option for that? Not entirely sure.

Well, there's vimium-ff [1] which works pretty good for me.

[1] https://addons.mozilla.org/en-US/firefox/addon/vimium-ff/

on which firefox version? have you tried nightly?

There's a blog post (the morning paper) about this: https://blog.acolyer.org/2017/09/13/adversarial-examples-for...

Yup, same: https://gist.github.com/Spotlight0xff/829b7ebf32c4feec60ec44...

I guess its because it was deleted by moderators instead of deleted by himself.

Seems to be explained here: https://www.netmarketshare.com/faq.aspx#Methodology

> Specific details of our network architecture will not be published at this time. DeepL Translator is based on a single, non-ensemble model.

Kinda sad to hear, but completely understandable. I'm curious whether the difference in performance is due to their model specifics or just better training data.

Does anyone have more information?

There's also a (simple) plugin for radare2!

https://github.com/radare/radare2-extras/tree/master/r2snowm...

I agree that browser integration is troublesome. To circumvent having to use a browser extension I use rofi-pass[0] which is a external script (using rofi/dmenu and pass), so no browser integration. But it features autofill which is extremely convenient.

[0] https://github.com/carnager/rofi-pass

Well, you don't have to be at a place with a SSH client for once. Its outputs to a webpage.

Honestly I think you are missing the point. The gift DeepMind made with the published self-play matches is not really aimed at the machine learning but rather to the Go community. And over at /r/baduk they are appreciating it, as it will allow them to gain further insights into the gameplay. So this is definitely _not_ 'nothing'.

You might like this article[0]. It definitely helped me develop some intuition for LSTMs.

0: http://r2rt.com/written-memories-understanding-deriving-and-...

Also in addition to the sibling comment, the published articles will be on github under their organization.

They did actually! [0] The blog posts are also online on their GitHub site.

[0] https://github.com/distillpub/template

Relevant Talk from GDC17: "Reach the Largest Gaming Platform of All: The Web. WebGL, WebVR and glTF" [0]

[0] https://www.youtube.com/watch?v=jLNtjujPhzY

I will look into updating the VAE example, as I've ported the example to the keras 2.0 API recently. There is currently no documentation on add_loss as far as I can see, so I will have to try a few things.