Neural Ideals and Neural Codes: An Algebraic Framework for Neural Network Classification and Feature Interpretation

Understanding the features captured by the hidden layers of neural networks is a fundamental challenge in machine learning, despite their widespread success across various classification problems. In this work, we propose an algebraic framework for examining neural networks that model classification