加热循环神经网络以最大限度地提高可达到的多态性,大大改善了学习
Gaspard Lambrechts1, Florent De Geeter1, Nicolas Vecoven1
1Montefiore Institute, University of Liège, 10 allée de la découverte, Liège, 4000, Belgium.
概括
训练循环神经网络 (RNN) 是具有长时间依赖性的挑战. 一种新的"升温"初始化技术增强了RNN的多稳定性,提高了学习速度和长期依赖任务的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 训练循环神经网络 (RNN) 对于具有长时间依赖性的任务是很困难的.
- 标准RNN细胞通常以有限的稳定平衡 (单稳定性) 进行初始化,阻碍学习.
- 学习长时间依赖性需要网络多稳定性,这是默认不容易实现的属性.
研究的目的:
- 调查网络平衡与RNN学习长期依赖之间的关系.
- 引入一种新的初始化程序",升温",以提高RNN学习长时间依赖性的能力.
- 设计一个改进的架构来学习长时间依赖性,同时保持精度.
主要方法:
- 在初始化时分析标准RNN细胞的稳定性和平衡性.
- 开发和应用"升温"初始化程序以最大限度地实现可实现的多稳定性.
- 评估信息归还,序列分类和强化学习基准的"升温"程序.
- 介绍和测试带有部分升温的双层架构.
主要成果:
- 网络多稳定性对于学习长期依赖性至关重要.
- "热身"初始化显著提高了RNN学习速度和长期依赖任务的性能.
- 虽然"升温"可以增强学习,但它有时会降低精度;双层架构中的部分升温可以减轻这一点.
- 现有的初始化方法隐含地促进了多稳定性.
结论:
- "升温"初始化程序是改善长时间依赖性的RNN学习的一般框架.
- 实现可实现的多稳定性是克服长序列RNN培训挑战的关键.
- 带有部分升温的双层架构提供了学习能力和精度之间的平衡.
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