Title: Deep Learning for Program Synthesis from Input-Output Examples

Abstract: There has been an emerging interest in applying machine learning-based techniques, especially deep neural networks, for program synthesis. However, because of some unique characteristics of the program domain, directly applying deep learning techniques developed for other applications is generally inappropriate. In this talk, I will present my work on program synthesis from input-output examples, aiming at synthesizing programs with higher complexity and better generalization. I will first discuss our work on execution-guided synthesis, where we develop approaches to leverage the execution results of both partial and full programs. In the second part of my talk, I will discuss our work on neural-symbolic architectures for compositional generalization.