Recent Advances in Neural Program Synthesis

By Neel Kant. 2018

39 citations recorded by the source corpus.

In recent years, deep learning has made tremendous progress in a number of fields that were previously out of reach for artificial intelligence. The successes in these problems has led researchers to consider the possibilities for intelligent systems to tackle a problem that humans have only recently themselves considered: program synthesis. This challenge is unlike others such as object recognition and speech translation, since its abstract nature and demand for rigor make it difficult even for human minds to attempt. While it is still far from being solved or even competitive with most existing methods, neural program synthesis is a rapidly growing discipline which holds great promise if completely realized. In this paper, we start with exploring the problem statement and challenges of program synthesis. Then, we examine the fascinating evolution of program induction models, along with how they have succeeded, failed and been reimagined since. Finally, we conclude with a contrastive look at program synthesis and future research recommendations for the field.

Subfields: Artificial Intelligence, Information Systems, Software, Natural Language Processing, Code Generation & Program Synthesis

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Citations in the corpus

A Survey of Machine Learning for Big Code and Naturalness (2017, background): “This is an interesting emerging area of research [101] but does not yet scale to the types of problems considered by the programming language and software engineering community [59, 69]; also see Devlin et al.”
Neural Shufﬂe-Exchange Networks − Sequence Processing in O( n log n ) Time (2019, background): “The design of memory access is crucial for learning algorithmic tasks, see (Kant, 2018) for a good overview.”
Synthetic Data for Deep Learning (2019, background): “The basic idea of teaching a machine learning model to program can be broadly divided into two subfields: program induction aims to train an end-to-end differentiable model to capture an algorithm [143], while program synthesis tries to teach a model the semantics of a domain-specific language (DSL)”