From Programs to Deep Models – Part 3: Code Completion
This post discusses the task of automatic code completion through the application of deep learning methods.
Archive of posts tagged: machine learning
Synthesizing Neurosymbolic Programs
We discuss the emerging problem of automatically synthesizing neurosymbolic programs, or programs that blend together neural networks and high-level code.
POPL 2020 in Review
POPL is the premiere conference on the theoretical foundations of programming languages. The PC Chair, General Chair, and Steering Committee Chair of POPL 2020 review this year’s event.
From Programs to Deep Models – Part 2
The wealth of code now available on-line is fertile ground to enable machine learning to be applied to programming tasks. This post is the second in a series on this topic, focusing on the tasks of semantically labeling and captioning code.
Understanding the World with Program Synthesis
Most applications of program synthesis are concerned with the engineering of software. However, because programming languages can be used to model the physical world, program synthesis can also offer a way of discovering and validating new hypotheses in the natural sciences. In this post, I elaborate on how.
