Training a First-Order Theorem Prover from Synthetic Data
  A major challenge in applying machine learning to automated theorem proving
is the scarcity of training data, which is a key ingredient in training
successful deep learning models. To tackle this problem, we propose an approach
that relies on training purely with synthetically generated theorems, without
any human data aside from axioms. We use these theorems to train a
neurally-guided saturation-based prover. Our neural prover outperforms the
state-of-the-art E-prover on this synthetic data in both time and search steps,
and shows significant transfer to the unseen human-written theorems from the
TPTP library, where it solves 72\% of first-order problems without equality.
