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arXiv · 2205.06547

Uninorm-like parametric activation functions for human-understandable neural models

Orsolya Csiszár·Luca Sára Pusztaházi·Lehel Dénes-Fazakas·Michael S. Gashler·Vladik Kreinovich·Gábor Csiszár
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Abstract

We present a deep learning model for finding human-understandable connections between input features. Our approach uses a parameterized, differentiable activation function, based on the theoretical background of nilpotent fuzzy logic and multi-criteria decision-making (MCDM). The learnable parameter has a semantic meaning indicating the level of compensation between input features. The neural network determines the parameters using gradient descent to find human-understandable relationships between input features. We demonstrate the utility and effectiveness of the model by successfully applying it to classification problems from the UCI Machine Learning Repository.

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Orsolya Csiszár, Luca Sára Pusztaházi, Lehel Dénes-Fazakas, Michael S. Gashler, Vladik Kreinovich, Gábor Csiszár. 2022-05-13. Uninorm-like parametric activation functions for human-understandable neural models. https://arxiv.org/abs/2205.06547

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