ffbd6cbb019a1413183c8d08f2929307-Supplemental.pdf

The numbers of the lower and upper bounds in the binarization layer are both in{5,10,50}. We utilize the Adam (Kingma and Ba, 2014) method for the training process with a mini-batch size of 32. Onlargedata sets, RRL is trained for 100 epochs, and we decay the learning rate by a factor of 0.75 every 20 epochs. Theinverse of regularization strength is in {1, 4, 16, 32}. Figure 7 shows the scatter plots of F1 score against log(#edges) for rule-based models trained on the other ten data sets.