Uninorm-like parametric activation functions for human-understandable neural models. (arXiv:2205.06547v1 [cs.AI])

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 …

ai arxiv human parametric