因果推理与深度学习相遇:一个全面的调查
Licheng Jiao1, Yuhan Wang1, Xu Liu1
1The School of Artificial Intelligence, Xidian University, Xi'an, China.
Research (Washington, D.C.)
|September 11, 2024
概括
深度学习模型可以被虚假的相关性误导. 本综述探讨了因果推理方法,灵感来自认知神经科学,以创建更强大的和可解释的深度学习模型.
科学领域:
- 人工智能的人工智能
- 认知神经科学 认知神经科学
- 机器学习 机器学习
背景情况:
- 深度学习模型从数据中学习,但可以捕捉虚假的相关性,限制可解释性和稳定性.
- 在深度学习中,基于相关性的传统模型面临着误导性数据模式的挑战.
研究的目的:
- 为深度学习提供因果推理方法的全面审查.
- 探索大脑启发的方法来增强深度学习模型的稳定性和可解释性.
主要方法:
- 审查与深度学习算法集成的因果推理技术.
- 讨论类似大脑的推断原理和因果学习概念.
- 检查大型模型任务和特定深度学习模式中的应用.
主要成果:
- 因果推理提供了一种途径,以减轻深度学习中的虚假相关性.
- 大脑启发的方法可以导致更稳定和可解释的AI模型.
- 整合因果推理可以提高深度学习的性能和可靠性.
结论:
- 因果推理对于推进强大和可解释的深度学习至关重要.
- 未来的研究应该专注于改进因果推理技术及其应用.
- 本调查提供了对因果深度学习研究的结构化概述和资源.
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