一个数学理论的关系概括在过渡性推理的数学理论
Samuel Lippl1,2,3, Kenneth Kay1,2,4, Greg Jensen1,3,5
1Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027.
概括
学习系统可以通过编码关系来执行过渡性推理 (TI),将它们推广到新的情况. 最小的统计原则解释了这种关系抽象,但丰富的神经网络在TI方面意外失败.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 人类和动物将学习到的关系推广到新的情况中,这是高级认知的一个关键方面.
- 过渡推理 (TI) 是一个经典的任务,证明了这种关系概括能力.
- 在学习系统中实现TI的潜在诱导偏见仍然不清楚.
研究的目的:
- 研究学习系统如何实现传递推理 (TI) 的诱导偏差.
- 分析数学模型以了解关系概括的机制.
- 解释 TI 任务和神经网络性能中观察到的行为模式.
主要方法:
- 对生物学相关的学习模型进行数学分析 (例如,梯度流,脊回归).
- 检查各项目添加值和一般表示.
- 分析规范最小化作为统计诱导偏差的分析.
- 评估神经网络在IT任务上的"丰富模式".
主要成果:
- 具有附加表示的生物相关模型自动编码过渡关系.
- 一个"连接性因子"和规范最小化解释了TI在固定表示中的概括.
- 丰富的神经网络由于局部编码机制而表现出不良的TI泛化.
结论:
- 最小的统计学学习原则,如规范最小化,可以产生过渡性.
- 信息技术的表现与代表性选择和诱导性偏见有关.
- 了解TI为关系抽象的神经基础提供了正式的见解.
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