Research at the intersection of machine learning, programming languages, and software
engineering has recently taken important steps in proposing learnable probabilistic models …

We survey recent work on neurosymbolic programming, an emerging area that bridges the
areas of deep learning and program synthesis. Like in classic machine learning, the goal …

Deep generative models have been wildly successful at learning coherent latent
representations for continuous data such as natural images, artwork, and audio. However …

Artificial Neural Networks are powerful function approximators capable of modelling
solutions to a wide variety of problems, both supervised and unsupervised. As their size and …

We develop a first line of attack for solving programming competition-style problems from
input-output examples using deep learning. The approach is to train a neural network to …

The problem of automatically generating a computer program from some specification has
been studied since the early days of AI. Recently, two competing approaches forautomatic …

Deep generative models have achieved remarkable success in various data domains,
including images, time series, and natural languages. There remain, however, substantial …

Recent years have seen the proposal of a number of neural architectures for the problem of
Program Induction. Given a set of input-output examples, these architectures are able to …

Transforming a graphical user interface screenshot created by a designer into computer
code is a typical task conducted by a developer in order to build customized software …

Program synthesis is the task of automatically finding a program in the underlying
programming language that satisfies the user intent expressed in the form of some …