  Recently, Diffenderfer and Kailkhura proposed a new paradigm for learning
compact yet highly accurate binary neural networks simply by pruning and
quantizing randomly weighted full precision neural networks. However, the
accuracy of these multi-prize tickets (MPTs) is highly sensitive to the optimal
prune ratio, which limits their applicability. Furthermore, the original
implementation did not attain any training or inference speed benefits. In this
report, we discuss several improvements to overcome these limitations. We show
the benefit of the proposed techniques by performing experiments on CIFAR-10.
