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arXiv · 2112.15026

Two Instances of Interpretable Neural Network for Universal Approximations

Erico Tjoa·Guan Cuntai
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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".

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Erico Tjoa, Guan Cuntai. 2021-12-30. Two Instances of Interpretable Neural Network for Universal Approximations. https://arxiv.org/abs/2112.15026

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