Mercer
3 long-form posts on Mercer: machine-learning research by Taha Bouhsine, each built around live, in-browser interactive visualizations.
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How to Interrogate a Kernel Network
A network whose hidden units are kernel prototypes is supposed to be legible. Legible claims are cheap unless someone can check them, so this post builds the checking: five instruments that put a trained Yat network under oath, each one asking a question that only this kernel makes askable. The first instrument finds that the softening constant in the formula sits ten thousand times below the distances it is supposed to soften, so the trained network never uses it at all: a term can be load-bearing in the theory and idle in the artifact, and only an audit tells you which.
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The Concept That Would Not Die
An empirical feature covariance gives a trained kernel network ranked orthogonal axes. Deleting one axis is an exact algebraic intervention; the experiment asks whether it is also a semantic one. It is not: the damage spreads broadly and a small probe recovers the targeted distinction.
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The Trained Network, Under Mercer's Microscope
Every hidden representation induces an empirical kernel. Decompose it into ranked modes, audit their stability and semantic evidence, then repeat the measurement on grayscale CIFAR-100 where one hundred classes leave room for a genuine concept-count test.