arXiv · 2509.15441
Computing Linear Regions in Neural Networks with Skip Connections
Abstract
Neural networks are important tools in machine learning. Representing piecewise linear activation functions with tropical arithmetic enables the application of tropical geometry. Algorithms are presented to compute regions where the neural networks are linear maps. Through computational experiments, we provide insights on the difficulty to train neural networks, in particular on the problems of overfitting and on the benefits of skip connections.
Explore related subjects
Keep this discovery
Johnny Joyce, Jan Verschelde. 2025-09-18. Computing Linear Regions in Neural Networks with Skip Connections. https://arxiv.org/abs/2509.15441
Cite the original work for its findings. Save a collection to share your selection of sources.