论文标题
使用直方图重新持续增长扩展机器学习分类功能
Extending machine learning classification capabilities with histogram reweighting
论文作者
论文摘要
我们建议使用蒙特卡洛直方图重新加权来推断机器学习方法的预测。在我们的方法中，我们将卷积神经网络的输出视为统计系统中可观察的，从而使其在参数空间中的连续范围内推断出来。我们使用二维ISING模型中的相跃迁来证明我们的建议。通过将神经网络的输出解释为订单参数，我们探索了系统中已知的可观察到的连接并研究其缩放行为。根据来自神经网络得出的数量进行有限尺寸缩放分析，该数量得出了对临界指数和临界温度的准确估计。该方法改善了从没有顺序参数的物理系统中的机器学习中获取精确度量的前景，而参数空间区域中直接采样的方法可能是不可能的。
We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learning methods. In our approach, we treat the output from a convolutional neural network as an observable in a statistical system, enabling its extrapolation over continuous ranges in parameter space. We demonstrate our proposal using the phase transition in the two-dimensional Ising model. By interpreting the output of the neural network as an order parameter, we explore connections with known observables in the system and investigate its scaling behaviour. A finite size scaling analysis is conducted based on quantities derived from the neural network that yields accurate estimates for the critical exponents and the critical temperature. The method improves the prospects of acquiring precision measurements from machine learning in physical systems without an order parameter and those where direct sampling in regions of parameter space might not be possible.
