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

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.

Semantic Interpretation of Deep Neural Networks Based on Continuous Logic

Combining deep neural networks with the concepts of continuous logic is desirable to reduce uninterpretability of neural models. Nilpotent logical systems offer an appropriate mathematical framework to obtain continuous logic based neural networks (CL neural networks). We suggest using a differentiable approximation of the cutting function in the nodes of the input layer as well as in the logical operators in the hidden layers. The first experimental results point towards a promising new approach of machine learning.