From Classical to Topological Neural Networks Under Uncertainty
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915790493581312 |
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| author | Dayton, Sarah Harkins Hamdan, Layal Bou Schizas, Ioannis D. Boothe, David L. Maroulas, Vasileios |
| author_facet | Dayton, Sarah Harkins Hamdan, Layal Bou Schizas, Ioannis D. Boothe, David L. Maroulas, Vasileios |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10266 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Classical to Topological Neural Networks Under Uncertainty Dayton, Sarah Harkins Hamdan, Layal Bou Schizas, Ioannis D. Boothe, David L. Maroulas, Vasileios Machine Learning Artificial Intelligence 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. |
| title | From Classical to Topological Neural Networks Under Uncertainty |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2602.10266 |