Tomita Y
Inverse renormalization group based on image super-resolution using deep convolutional networks
The inverse renormalization group is studied based on the image super-resolution using the deep convolutional neural networks. We consider the improved correlation configuration instead of spin configuration for the spin models, such as the two-dimensional Ising and three-state Potts models.
ReadMachine-learning study using improved correlation configuration and application to quantum Monte Carlo simulation
We use the Fortuin-Kasteleyn representation-based improved estimator of the correlation configuration as an alternative to the ordinary correlation configuration in the machine-learning study of the phase classification of spin models.
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