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.
Archive of posts tagged: machine learning
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.
Machine Learning and Approximate Computing
There’s a new ecosystem of deep-learning-driven applications, occasionally titled Software 2.0, that integrates neural networks into a variety of computational tasks. Such applications include image recognition, natural language processing, and other traditional...
From Programs to Deep Models – Part 1
The wealth of code now available on-line is fertile ground to enable machine learning to be applied to programming tasks. This post discusses the promise of and some progress on the problem “deep code.” It is the first in a series.
