Faster Learning in Inclusive-Exclusive Integral Neural Networks

Abstract

Inclusion-exclusion integral neural network is an explainable network model using inclusion-exclusion integral defined by fuzzy measures and polynomial operations. Inclusion-exclusion integral neural networks increase the expressiveness of the network by specifying a large additivity, but the number of parameters increases exponentially, and the training time increases accordingly. This is one of the challenges. The objective of this research is to accelerate the learning of inclusion-exclusion integral neural networks by comparing the speed between CPU and GPU in terms of additivity and the number of data, and by applying acceleration methods in PyTorch.