  Machine learning has been successfully applied to identify phases and phase
transitions in condensed matter systems. However, quantitative characterization
of the critical fluctuations near phase transitions is lacking. In this study
we propose a finite-size scaling approach based on a convolutional neural
network and analyze the critical behavior of a quantum Hall plateau transition.
The localization length critical exponent learned by the neural network is
consistent with the value obtained by conventional approaches. We show that the
general-purposed method can be used to extract critical exponents in models
with drastically different physics and input data, such as the two-dimensional
Ising model and 4-state Potts model.
