In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …

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 …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Recent work learns contextual representations of source code by reconstructing tokens from
their context. For downstream semantic understanding tasks like summarizing code in …

Recent Language Models (LMs) achieve breakthrough performance in code generation
when trained on human-authored problems, even solving some competitive-programming …

Program synthesis from input-output (IO) examples has been a long-standing challenge.
While recent works demonstrated limited success on domain-specific languages (DSL), it …

Generative neural models hold great promise in enhancing programming education by
synthesizing new content. We seek to design neural models that can automatically generate …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

The use of deep learning techniques has achieved significant progress for program
synthesis from input-output examples. However, when the program semantics become more …