This approach shares a lot in common with the idea of multivariate interpolation over scattered data. Multivariate interpolation attempts to estimate values at unknown points within an existing data set and is often used in fields such as geostatistics or for geophysical analysis like elevation modelling. We can think of our colour palette as the set of variables we want to interpolate from, and our input colour as the unknown we’re trying to estimate. We can borrow some ideas from multivariate interpolation to develop more effective dithering algorithms.
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第四十三条 下列纳税人可以适用增值税法第三十条规定的以一个季度为一个计税期间：
Owain Evans’ idea of feeding a historical LLM non-anachronistic images is, I think, well worth doing. But it’s also worth expanding on further. Would it be helpful, when training a historical LLM, to simulate dream imagery based on premodern themes? What about audio of birdcalls, which were far more prominent in the audioscapes of premodern people? What about taking it on a walk through the woods?