joerick

I only just saw it mentioned in this thread, but CinderX might suit you well.

I have a Focusrite Saffire that has lots of nice, quiet preamps and ADC channels. I managed to get an old Mac to connect over FireWire and using the Focusrite Control app, I configured the routing to map all the analog inputs to ADAT. That works great but it's always at the wrong sample rate, and I can't change that without getting the old Mac out. Maybe I'll look into one of these rPi shields too. Anyone got any reverse engineering tips for the control protocol?!

I've been wondering for a while if a program could "learn" covariance somehow. Through real-world usage.

Otherwise, it feels to me that it'd be consistently wrong to model the variables as independent. And any program of notable size is gonna be far too big to consider correlations between all the variables.

As for how one might do the learning, I don't know yet!

If we assume no conspiracy, what's the sentencing like for perjury due to carelessness?

Beautifully put.

Pretty fascinating from an information theory point of view. Surprising that it works at all. Is this, like, the JPEG of uniformly distributed, uncorrelated data?

I came across this page in a discussion about dithering. It was posted by @rikroots.

Fascinating stuff. I love to see the application of the 2D Fourier transform for analysis. Added bonus - they tile brilliantly.

Hahaha. We used to get desyncs on networked games of Generals pretty regularly. I remember if a game took more than 30-40 mins I'd start to get a spidey sense things were about to go wrong

Yeah, 'everything' as in 'everything that the model cares about' :)

Nice workaround. I just wish there was a less 'lossy' way to go about it!

Interesting, but your hypothesis assumes that 'tone' is one-dimensional, that there is a single axis you can remove. I think tone is very multidimensional, I'd expect to be removing multiple 'directions' from the embedding.

The thing that puzzles me about embeddings is that they're so untargeted, they represent everything about the input string.

Is there a method for dimensionality reduction of embeddings for different applications? Let's say I'm building a system to find similar tech support conversations and I am only interested in the content of the discussion, not the tone of it.

How could I derive an embedding that represents only content and not tone?