关于神经网络的不确定性原则
Jun-Jie Zhang1, Dong-Xiao Zhang1, Jian-Nan Chen1
1Northwest Institute of Nuclear Technology, Xi'an, Shaanxi 710024, China.
iScience
|March 31, 2025
概括
神经网络面临着准确性和稳定性之间的权衡,类似于量子力学的不确定性原理. 这限制了它们抵制敌对攻击的能力,同时学习复杂的特征.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 量子物理的类比 量子物理的类比
背景情况:
- 神经网络经常在预测准确性和对抗攻击的稳定性之间进行权衡.
- 现有的研究突出了神经网络对复杂操纵技术的脆弱性.
- 这种准确性-稳定性困境的理论基础仍然不完全理解.
研究的目的:
- 研究神经网络中准确性和稳定性之间的固有权衡.
- 建立一个理论框架,与量子力学的不确定性原理进行并行,以解释这种权衡.
- 探索实现人类级别智能的影响,并设计更强大的AI系统.
主要方法:
- 开发了一个基于量子力学不确定性原理的理论模型.
- 使用数学证明来证明神经网络中存在不确定性关系的存在.
- 进行实证实验以验证理论发现并分析网络行为.
主要成果:
- 证明神经网络同时优化准确性和稳定性的能力存在根本限制.
- 展示了在训练过程中明确的阶级界限自然会导致这种准确性-稳定性权衡.
- 证实了互补原则对神经网络学习结合特征的适用性.
结论:
- 神经网络中的准确性-稳定性权衡是它们学习动态的自然结果,类似于量子不确定性.
- 由于这些基本限制,实现人类级别的智能可能需要超越单个网络或庞大的数据集的架构.
- 这些发现为神经网络漏洞提供了新的理论见解,并指导开发更强大的AI架构.
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