GENEOnet通过统计分析验证了其在生物化学中的可解释性与可靠性。
GENEOnet: Statistical analysis supporting explainability and trustworthiness
- 基于群等变非扩张算子构建，提升模型可解释性
- 相比其他方法，等变性比例显著更高
- 对分子动力学扰动平均具有鲁棒性，适合可信AI场景
群等变非扩张算子（GENEOs）已成为构建机器学习与人工智能网络的数学工具。最新研究表明，这类模型因固有的可解释性，可融入可解释人工智能（XAI）领域。本研究旨在通过多种统计分析和实验验证GENEOnet——一个用于计算生物化学应用的GENEO网络——的这一主张。实验首先对GENEOnet参数进行敏感性分析，检验其重要性；随后证明GENEOnet的等变性比例显著高于其他方法；最后表明，GENEOnet在分子动力学扰动下平均表现出强鲁棒性。这些结果共同证实了GENEOnet在可解释性、可信性与鲁棒性方面的优势，支持了在可信人工智能背景下使用GENEOs的有益性。
原文摘要 · Abstract (English)
Group Equivariant Non-Expansive Operators (GENEOs) have emerged as mathematical tools for constructing networks for Machine Learning and Artificial Intelligence. Recent findings suggest that such models can be inserted within the domain of eXplainable Artificial Intelligence (XAI) due to their inherent interpretability. In this study, we aim to verify this claim with respect to GENEOnet, a GENEO network developed for an application in computational biochemistry by employing various statistical analyses and experiments. Such experiments first allow us to perform a sensitivity analysis on GENEOnet's parameters to test their significance. Subsequently, we show that GENEOnet exhibits a significantly higher proportion of equivariance compared to other methods. Lastly, we demonstrate that GENEOnet is on average robust to perturbations arising from molecular dynamics. These results collectively serve as proof of the explainability, trustworthiness, and robustness of GENEOnet and confirm the beneficial use of GENEOs in the context of Trustworthy Artificial Intelligence.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。