用拓扑与不确定性建模提升模型在军事场景下的鲁棒性
From Classical to Topological Neural Networks Under Uncertainty
- 融合拓扑分析与贝叶斯方法，构建感知不确定性的神经网络
- 在图像、时序与图数据上实现更强的泛化与可解释性
- 适合关注军事智能、安全可信AI的研究者
本章探讨神经网络、拓扑数据分析及拓扑深度学习技术，结合统计贝叶斯方法，用于处理图像、时间序列和图数据，以最大化人工智能在军事领域的潜力。通过展示图像、视频、音频和时序识别、欺诈检测及图数据链路预测等实际应用，说明拓扑感知与不确定性感知模型如何增强模型的鲁棒性、可解释性与泛化能力。
原文摘要 · Abstract (English)
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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