基于随机共振的自动编码器,用于无监督学习
Yuhao Ren1, Fabing Duan1, François Chapeau-Blondeau2
1Institute of Complexity Science, <a href="https://ror.org/021cj6z65">Qingdao University</a>, Qingdao 266071, People's Republic of China.
Physical review. E
|August 20, 2024
概括
将噪音添加到神经元网络中可以增强信息表示,从而导致自我关闭的随机共振. 这种噪音增强模型可以提高无监督学习中的自动编码器性能.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 麦卡洛克-皮茨神经元是神经网络中的基本单元.
- 神经网络中的信息处理可能受到噪音的限制.
研究的目的:
- 为神经网络引入一个统一的噪声增强激活模型.
- 为了研究自我关门的随机共振现象.
- 展示该模型在无监督学习中的应用.
主要方法:
- 将添加式噪声组件纳入麦卡洛奇-皮茨神经元组合.
- 将输入乘以自动关门的平均发射概率.
- 设计具有噪声增强激活功能的自动编码器.
主要成果:
- 添加噪声增强了信息表示,接近平均发射概率.
- 通过自编码器损失优化观察到的自闭式随机共振.
- 在非零噪声缩放超参数下实现最佳性能.
结论:
- 通过噪声增强的激活模型与自闭门增强神经网络的性能.
- 自动关闭的随机共振是改善无监督学习的关键因素.
- 这种方法显示了推进无监督学习技术的潜力.
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