Ood
2 long-form posts on Ood: machine-learning research by Taha Bouhsine, each built around live, in-browser interactive visualizations.
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Calibrating a Bounded Net, in JAX/Flax NNX
A runnable companion: build the matched Yat and ReLU MLPs in Flax NNX with the same softmax head, then measure their honesty. The reliability diagram and ECE, temperature scaling fit on a held-out split, NLL and Brier, and the two out-of-distribution channels, kernel-field magnitude versus softmax confidence, all in JAX with every number from a real three-seed run.
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When 80% Should Mean 80%
A network hands you a probability with every answer, and the number is the part you act on. So when this series' bounded, self-explaining kernel network says 80%, is that a measurement or a mood? Five posts of evidence say it should be the honest one. This post puts that reputation through a lie-detector test, reliability diagrams, expected calibration error and temperature scaling against a matched ReLU MLP on Fashion-MNIST, and what the test found is the post.