自然梯度使得在尖端神经网络中快速采样成为可能
Paul Masset1,2, Jacob A Zavatone-Veth1,3, J Patrick Connor4
1Center for Brain Science, Harvard University Cambridge, MA 02138.
Advances in neural information processing systems
|July 21, 2023
概括
尖端神经网络现在可以使用人口几何学进行快速的概率推理. 这项研究统一了采样方法,并为神经形态计算和神经生物学提供了见解.
科学领域:
- 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:
- 动物需要不确定性估计来在动态环境中导航.
- 在生物学上可信的尖端神经网络中实施快速采样算法仍然是一个挑战.
研究的目的:
- 提出一种方法,用于在尖端神经网络中实施快速采样算法.
- 在一个共同的框架内统一不同类别的尖端采样器.
- 在高维分布中演示快速参数推断.
主要方法:
- 利用人口几何学,神经代码和神经动力学.
- 模拟Langevin采样,使用高效的平衡刺网络.
- 实施大都会 - 黑斯廷斯采样与概率峰值规则.
主要成果:
- 统一了两类尖端采样器 (Langevin和Metropolis-Hastings) 的两个类.
- 从强烈相关的高维分布中快速推断参数.
- 展示了人口几何学对推理速度的影响.
结论:
- 人口几何学是快速概率推理神经网络的关键.
- 为基于采样的推理算法提供设计原则.
- 为神经形态计算和可测试的神经生物学预测提供了潜在的灵感.
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