autodiff

SpeakEZ.tech ✓ @SpeakezTech · Mar 29 The PCA/random projection tradeoff makes sense for static datasets where structure can be analyzed in advance. In gradient estimation via randomized forward-mode autodiff, you cannot run PCA on the object you are trying to compute. Random projection is not a fallback there. It is the only non-circular approach. The J-L bound then tells you exactly how many directional samples you need, logarithmic in weight-space dimension. When accumulation is exact via quire, the distortion bound is purely statistical with no arithmetic error component folded in.