Lazy Training
2 long-form posts on Lazy Training: machine-learning research by Taha Bouhsine, each built around live, in-browser interactive visualizations.
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Lazy Training a Yat Network in JAX/Flax NNX
A runnable companion to the lazy-training post: the Yat layer with its frozen softplus scalars, the one-line NNX filter that trains a readout while the bank stays frozen, the per-arm learning-rate bracketing, and the movement telemetry that caught the anti-lazy power law. With the run's own prototype trajectories: a Gram matrix crystallizing as neurons accumulate, two readouts racing on frozen banks, and eight random prototypes drifting through training without ever becoming pictures.
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How Many Random Neurons Buy a Trained One?
Freeze a bank of randomly initialized Yat units and train only the linear readout. The induced random-feature kernel is a Monte Carlo average; under finite variance its estimation error has the familiar square-root scaling. This post measures that exponent, counts how many frozen units buy each rung of an accuracy ladder, and then unfreezes the centers to measure what feature learning adds.