4 posts found
For continuous stationary positive-definite kernels, Bochner’s theorem turns kernel design into a choice of spectral measure. We derive random Fourier features, connect positional rotations to frequency, and explain where the bridge to attention and spectral transformers needs extra assumptions.
A practitioner-level synthesis of twelve weeks of tutorials through one lens — spectral bias, Fourier features, FFT-based token mixing, Bochner's theorem, and neural operators — showing that positional encodings, kernels, state-space models and diffusion schedules are the same idea in different clothes.
A step-by-step introduction to Gaussian Processes — what they are, how kernel functions define them, how to do GP regression, and how Deep GPs extend them to multi-layer architectures.
How scaled dot-product attention is secretly Nadaraya–Watson kernel regression — and what that reveals about the GP–Transformer duality, the Deep GP revival, and why uncertainty is the missing ingredient for world models.