Philip Chou

Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors

We build interpretable and lightweight transformer-like neural networks by unrolling iterative optimization algorithms that minimize graph smoothness priors---the quadratic graph Laplacian regularizer (GLR) and the $\ell_1$-norm graph total variation (GTV)---subject to an interpolation constraint. T…