Adaptive Step
2 long-form posts on Adaptive Step: machine-learning research by Taha Bouhsine, each built around live, in-browser interactive visualizations.
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An Error Controller for a Trained Net, in JAX
A runnable companion to the depth-on-demand post: the leapfrog classifier trained with lax.scan at fixed depth, the step-doubling controller that re-renders it to tolerance at inference, the honest work accounting (probes included), and the measured tol^(-1/3) power law. Every figure is rendered from the real Kaggle run.
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Depth on Demand
The last post made depth a resolution: layers are time steps of a learned flow, and running more of them just renders the same trajectory finer. But every camera knows not to spend equal film on empty sky. This post gives a trained network the integrator's next tool, an error controller that chooses its own step size per input, with no retraining: the same weights, rendered to tolerance. The controller reproduces the reference verdicts at a fraction of the steps, its cost follows the integrator's textbook one-third power law, and the map of where it spends is a genuine surprise: effort tracks the stiffness of the learned flow, not the difficulty of the classification.