5 docs tagged with "sampling"

A successive-approximation ADC is, physically, a capacitor with a switch in front of it. Converting a voltage happens in two completely different phases: first the switch closes and the capacitor is allowed to charge towards your signal through the source impedance, and then the switch opens and a comparator plays twelve rounds of twenty questions against the trapped charge. The second phase is fixed by the hardware and takes exactly twelve clocks. The first phase is the one you configure, the one every tutorial leaves at its reset value, and the one that decides whether your reading means anything at all.

A trained diffusion model is only half the system. Diffusion Models trains a noise predictor — but how you turn that predictor into actual samples, how many steps you take, and what you condition on are all choices made after training, and they are where most of the practical control lives.

The model outputs a probability distribution over the entire vocabulary at every step — something has to turn that distribution into an actual sequence of chosen tokens. That "something" is decoding, and it's a design decision entirely separate from the model itself: the same weights, decoded two different ways, can produce text that reads as either robotic and repetitive or lively and varied.

DDPM Sampling and Guidance got diffusion sampling down to tens of steps. This page covers the research direction aimed squarely at pushing that further — toward single-digit, and eventually single-step, generation, by rethinking what the network is trained to predict in the first place.

"Find the k largest" looks like a sorting problem, and sorting solves it — but sorting also computes