通过直方图重新加权扩展机器学习分类功能
Extending machine learning classification capabilities with histogram reweighting
摘要：
我们建议使用蒙特卡洛直方图重新加权来外推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的可观察值，从而使其能够在参数空间中的连续范围内进行外推。我们使用二维伊辛模型中的相变来证明我们的建议。通过将神经网络的输出解释为序参量，我们探索了系统中与已知观测量的联系，并研究了其标度行为。基于从神经网络得到的量进行有限尺寸标度分析，该神经网络产生临界指数和临界温度的精确估计。该方法改善了在没有序参量的物理系统中从机器学习获取精确测量的前景，以及在参数空间区域中直接采样可能不可能的那些系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
