From Classical to Topological Neural Networks Under Uncertainty

Sarah Harkins Dayton, Layal Bou Hamdan, Ioannis D. Schizas, David L. Boothe, Vasileios Maroulas
2/10/2026
cs.LGcs.AI

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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.

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Cite this paper

@article{dayton2026from,
  title  = {From Classical to Topological Neural Networks Under Uncertainty},
  author = {Sarah Harkins Dayton and Layal Bou Hamdan and Ioannis D. Schizas and David L. Boothe and Vasileios Maroulas},
  year   = {2026},
  eprint = {2602.10266},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2602.10266v1}
}

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