Yat
4 long-form posts on Yat: machine-learning research by Taha Bouhsine, each built around live, in-browser interactive visualizations.
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Your Neuron Is a Picture, in JAX/Flax NNX
A runnable companion: build the prototype MLP from the post in Flax NNX, train it on Fashion-MNIST, and watch the neurons. Pull the prototypes out as images, read a prediction as a vote over pictures, see the model abstain on out-of-distribution digits, check that random-init prototypes classify but stay noise, and track the prototypes migrating through a UMAP fit on the dataset as they train.
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Your Neuron Is a Direction. It Should Be a Picture.
Why should a neuron store a direction when it could store a thing? A direction is not a referent you can point at, which is why MLPs are opaque. Put the Yat kernel where the activation was, train on Fashion-MNIST, and every neuron becomes a prototype that lives in pixel space, literally a picture, so the network reads its own predictions: this looks like that, no saliency method required.
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The Yat-Kernel MLP in JAX/Flax NNX
A runnable companion to What a Finite Kernel Buys an MLP: build a layer whose unit is the Yat kernel instead of a linear map plus an activation, assert it is positive definite and nonnegative, write down its exact finite feature map, train it end-to-end on two moons with no activation function, and measure the lazy-loading sparsity, the bounded off-distribution response, the RKHS capacity, and the force field that pulls each prototype onto its data.
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What a Finite Kernel Buys an MLP
Replace the activation function with a finite, explicit, positive-definite kernel, the Yat kernel, and an MLP stops being a stack of linear maps glued by a nonlinearity. It becomes a kernel machine, with locality, attribution, geometry, capacity control, and a feature map you can write down.