2022
DOI: 10.48550/arxiv.2205.06547
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Uninorm-like parametric activation functions for human-understandable neural models

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 featu… Show more

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