LGAIFeb 10

From Classical to Topological Neural Networks Under Uncertainty

arXiv:2602.10266v1
Originality Synthesis-oriented
AI Analysis

It addresses improving AI reliability and interpretability for military decision-makers, but appears incremental as it synthesizes existing techniques.

This chapter explores how combining topological data analysis and Bayesian methods with neural networks can enhance AI robustness and interpretability for military applications like image recognition and fraud detection, though no specific performance numbers are provided.

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.

Foundations

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