Learning a Meta-Solver for Syntax-Guided Program Synthesis

Representation learning and transfer learning of structured data with rich semantics remain open problems. Our work addresses two fundamental challenges in this area: q How to learn a neural representation of both syntax and semantic constraints? q How to learn a transferable policy for program synthesis tasks with different syntax G and semantic φ? Cryptographic circuits synthesis Synthesize programs using a grammar adaptive policy network Jointly learn the representation of syntax and semantics