From Classical to Topological Neural Networks Under Uncertainty

TL;DR

This paper reviews the integration of topological data analysis and Bayesian methods into neural networks to improve robustness, interpretability, and generalization in AI applications across various data types.

Contribution

It introduces a comprehensive overview of topology-aware and uncertainty-aware neural network techniques for diverse AI tasks in the military domain.

Findings

Topology-aware models enhance robustness against data variability

Uncertainty-aware models improve interpretability and generalization

Applications include image, video, audio, and graph data recognition

Abstract

This chapter explores neural networks, topological data analysis, and topological deep learning techniques, alongside statistical Bayesian methods, for processing images, time series, and graphs to maximize the potential of artificial intelligence in the military domain. Throughout the chapter, we highlight practical applications spanning image, video, audio, and time-series recognition, fraud detection, and link prediction for graphical data, illustrating how topology-aware and uncertainty-aware models can enhance robustness, interpretability, and generalization.