可解释的AI通过学习优化来优化
1Typal Academy, Richland, USA. research@typal.academy.
Scientific reports
|June 21, 2023
概括
本研究介绍了可解释人工智能 (XAI) 的"学习优化" (L2O),提供透明的机器学习模型,编码先前知识并提供可验证的可信度. 应用包括信号恢复和医学成像.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 机器学习模型经常充当"黑子",阻碍对关键应用程序的信任和采用.
- 越来越需要可解释的人工智能 (XAI),以提供透明度和可解释性.
- 编码先验知识和标记不值得信赖的推断是当前XAI的关键挑战.
研究的目的:
- 为XAI开发具体的工具,允许编码先前的知识.
- 提出标记机器学习模型中不可靠推断的方法.
- 提高数据驱动算法的透明度和可解释性.
主要方法:
- 利用"学习优化" (L2O) 方法论,将推理作为数据驱动的优化问题.
- 实施的L2O模型直接结合了先前的知识并提供了理论保证.
- 引入可解释的证书来验证模型推理的可靠性.
主要成果:
- L2O模型展示了直接实现和直接编码先前知识的简单性.
- 实现了理论上的保证,例如约束满足.
- 信号恢复,CT成像和加密资产交易中的数值示例验证了这一方法.
结论:
- L2O方法为构建可解释的人工智能系统提供了实际框架.
- 可解释证书提高了AI推断的可信度和可验证性.
- 这种方法为XAI在各种科学和金融领域提供了有希望的方向.
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