2 papers
cs.LG2022
Implicit Mixture of Interpretable Experts for Global and Local Interpretability
Nathan Elazar, Kerry Taylor
We investigate the feasibility of using mixtures of interpretable experts (MoIE) to build interpretable image classifiers on MNIST10. MoIE uses a black-box router to assign each in…
cs.LG2022
Conditional Autoregressors are Interpretable Classifiers
Nathan Elazar
We explore the use of class-conditional autoregressive (CA) models to perform image classification on MNIST-10. Autoregressive models assign probability to an entire input by combi…