Theory
4 long-form posts on Theory: machine-learning research by Taha Bouhsine, each built around live, in-browser interactive visualizations.
-
A White-Box Kernel FFN in JAX/Flax NNX
A runnable companion: build a transformer whose feed-forward block is a finite learned-center kernel expansion. Train it on tinyshakespeare, then read each memory slot, attribute outputs exactly, edit one slot, and test peak kernel response as an abstention score.
-
The MLP Block Can Be a Kernel Memory
Replace an MLP activation with a kernel and its feed-forward block becomes an explicit learned-center expansion. Its slots can be read, attributed, and edited, but this architectural parameterization is not the classical representer theorem.
-
What Can a Weight Be?
A kernel is a spectral price list: it decides which functions are affordable, and regularization sets the budget. Compare Sobolev, Gaussian, spherical, and finite examples, then connect their eigenvalues to kernel ridge shrinkage and effective dimension.
-
Where Does a Weight Live?
A standard neuron's weight and its input never actually meet: one is a point you can see, the other an arrow off in its own space, joined only by a shadow. This is what a reproducing kernel Hilbert space fixes: it gives input and weight one shared address, where the optimal weight is built from the data itself and sits right next to it. Four interactive panels.