Feed
The Spectral Neuron
A neuron that outputs an eigenvalue of a learned matrix, staying readable as it grows.
How it works
Transparency or power
Linear models show what each feature does but stay simple. Neural nets scale in power but hide how features act.
Eigenvalue as nonlinearity
Each feature scales a learned symmetric matrix. The sum is one matrix, and its k-th smallest eigenvalue is the prediction.
Scales, stays readable
Bigger matrices fit harder functions. Results land near MLPs on HIGGS and factorization machines on Criteo, with explicit influence bounds.
From the paper