Autoencoders as Tools for Program Synthesis
Recently there have been many advances in research on language modeling of source code. Applications range from code suggestion and completion to code summarization. However, complete program synthesis of industry-grade programming languages remains an open problem. In this work, we introduce and experimentally validate a variational autoencoder model for program synthesis of industry-grade programming languages. This model makes use of the inherent tree structure of code and can be used in conjunction with gradient free optimization techniques like evolutionary methods to generate programs that maximize a given fitness function, for instance, passing a set of test cases. A demonstration is avaliable at https://tree2tree.app
Code (1)
Tasks
Code SummarizationLanguage ModelingLanguage ModellingProgram SynthesisSimilar Papers 제목 키워드 기반
Grammar Filtering For Syntax-Guided Synthesis
Programming-by-example (PBE) is a synthesis paradigm that allows users to generate functions by simply providing input-output examples. While a promising interaction paradigm, synthesis is still too slow for realtime int…
BIG-bench Machine LearningHerb.jl: A Unifying Program Synthesis Library
Program synthesis -- the automatic generation of code given a specification -- is one of the most fundamental tasks in artificial intelligence (AI) and the dream of many programmers. Numerous synthesizers have been devel…
Program SynthesisUnsupervised Learning by Program Synthesis
We introduce an unsupervised learning algorithmthat combines probabilistic modeling with solver-based techniques for program synthesis.We apply our techniques to both a visual learning domain and a language learning prob…
Program SynthesisThe Challenges in Specifying and Explaining Synthesized Implementations of Reactive Systems
In formal synthesis of reactive systems an implementation of a system is automatically constructed from its formal specification. The great advantage of synthesis is that the resulting implementation is correct by constr…
Anomaly detection with spiking neural networks for LHC physics
Anomaly detection offers a promising strategy for discovering new physics at the Large Hadron Collider (LHC). This paper investigates AutoEncoders built using neuromorphic Spiking Neural Networks (SNNs) for this purpose.…
Anomaly Detection