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andrew3726

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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.

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.

Specific details of our network architecture will not be published at this time.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?

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'.

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.