Tag
Proposes Trans-Ising, a transfer learning method for high-dimensional Ising models that uses a loss-based source screening rule and two-stage estimation to improve estimation accuracy over target-only and naive pooling methods.
This paper presents a scalable backpropagation-based algorithm for training deep convolutional networks to run on thermodynamic Ising hardware, achieving 94.9% on CIFAR-10 and 76.0% on CIFAR-100 while analyzing inference cost-accuracy tradeoffs.