🤖 AI Summary
This work addresses the critical challenges of robustness, generalization, and interpretability faced by artificial intelligence models in high-stakes domains such as defense, particularly when handling diverse data modalities including images, time series, and graph-structured data. To this end, the study proposes a unified framework that, for the first time, systematically integrates topological neural networks, topological data analysis (TDA), and Bayesian deep learning. This integration enables simultaneous capture of complex intrinsic geometric structures within data and principled quantification of model uncertainty. The resulting approach demonstrates significant performance improvements across a range of tasks—including image, video, and audio recognition, fraud detection, and graph link prediction—thereby delivering more reliable, interpretable, and generalizable AI solutions for mission-critical applications.
📝 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.