Extending machine learning classification capabilities with histogram reweighting

Understand

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.