Two Instances of Interpretable Neural Network for Universal Approximations

Code

• github.com/ericotjo001/explainable_ai
  OfficialIn paperpytorch★ 4
• github.com/etjoa003/explainable_ai
  pytorch★ 4

Abstract

This paper proposes two bottom-up interpretable neural network (NN) constructions for universal approximation, namely Triangularly-constructed NN (TNN) and Semi-Quantized Activation NN (SQANN). Further notable properties are (1) resistance to catastrophic forgetting (2) existence of proof for arbitrarily high accuracies (3) the ability to identify samples that are out-of-distribution through interpretable activation "fingerprints".

Tasks

Reproductions