人工和大脑神经网络中的表示和概括
Qianyi Li1,2, Ben Sorscher3, Haim Sompolinsky2,4
1The Harvard Biophysics Graduate Program, Harvard University, Cambridge, MA 02138.
概括
生物和人工深度神经网络 (DNN) 显示出不同的概括能力. 神经多元体的几何性质和DNN中的学习理论为DNN如何从有限的数据中改进概括提供了见解.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 认知科学 认知科学
背景情况:
- 人类和动物有效地从有限的数据中进行概括,这是人工智能尚未达到的壮举.
- 在生物和人工深度神经网络 (DNN) 中的概括对于现实世界的应用至关重要,包括分布式和分布式外的场景.
研究的目的:
- 研究生物和人工深度神经网络 (DNN) 中的概括.
- 提出将神经多元体几何学和DNN学习理论与概括能力联系起来的假设.
- 通过统一的方法学来弥合神经科学,机器学习和认知科学.
主要方法:
- 概述最近在研究神经多元体的几何学方面的进展,特别是在视觉对象识别方面.
- 讨论将多重维度和半径连接到概括能力的理论.
- 探索广泛DNN中的学习理论,包括重量规范规范化,网络架构和超参数的作用.
主要成果:
- 神经多元体几何学,特别是它的几何性质,作为一个序列参数,将神经基质与概括联系起来.
- 在广泛的DNN中学习的理论为生成所需的神经表示几何和概括提供了机械的见解.
- 重量规范规范化,网络架构和超参数在DNN概括中发挥着重要作用.
结论:
- 神经多元体的几何性质是理解生物和人工系统中概括的关键.
- 在广泛的DNNs中学习理论为改善概括能力提供了一个机制框架.
- 进一步研究代表性漂移和学习动态对于推进人工智能泛化至关重要.
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