Can Neural Networks Learn Symbolic Rewriting?
Table 5: Chosen results of experiments with polynomials. (Characteristic of formulas concerns the input polynomials. Labels of the data sets correspond to folder names in the Github repo.)
['Label', 'Function symbols', 'Constant symbols', 'Num. of variables', 'Accuracy on test']
Then experiments on more challenging but also much larger data sets for polynomial normalization were performed. Depending on the difficulty of the data, accuracy on the test sets achieved in our experiments varied between 70% and 99%.