Events

CS Colloquium: Learning-Based Program Synthesis: Learning for Program Synthesis and Program Synthesis for Learning

Speaker: Xinyun Chen

Location: 60 Fifth Avenue, Room 150
Videoconference link: https://nyu.zoom.us/j/96193237398

Date: Tuesday, April 5, 2022

With the advancement of modern technologies, programming becomes
ubiquitous not only among professional software developers, but also for
general computer users. However, gaining programming expertise is
time-consuming and challenging. Therefore, program synthesis has many
applications, where the computer automatically synthesizes programs from
specifications such as natural language descriptions and input-output
examples. In this talk, I will present my work on learning-based program
synthesis, where I have developed deep learning techniques for various
program synthesis problems. Despite the remarkable success of deep
neural networks for many domains, including natural language processing
and computer vision, existing deep neural networks are still
insufficient for handling challenging symbolic reasoning and
generalization problems.

My learning-based program synthesis research lies in two folds: (1)
learning to synthesize programs from potentially ambiguous and complex
specifications; and (2) neural-symbolic learning for language
understanding. I will first talk about program synthesis applications,
where my work demonstrates the applicability of learning-based program
synthesizers for production usage. I will then present my work on
neural-symbolic frameworks that integrate symbolic components into
neural networks, which achieve better reasoning and generalization
capabilities. In closing, I will discuss the challenges and
opportunities of further improving the complexity and generalizability
of learning-based program synthesis for future work.