most citedSCELMo: Source Code Embeddings from Language Models

34 citations · 55 across the 3 of their papers we have counts for

collaborators

7 papers

stat.ML20203 cited

Neural Program Synthesis with a Differentiable Fixer

Matej Balog, Rishabh Singh, Petros Maniatis +1

We present a new program synthesis approach that combines an encoder-decoder based synthesis architecture with a differentiable program fixer. Our approach is inspired from the fac…

cs.SE202034 cited

SCELMo: Source Code Embeddings from Language Models

Rafael - Michael Karampatsis, Charles Sutton

Continuous embeddings of tokens in computer programs have been used to support a variety of software development tools, including readability, code search, and program repair. Cont…

cs.SE2020

Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code

Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes +2

Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new software development tools, such as tools for code s…

cs.LG2020

Towards Modular Algorithm Induction

Daniel A. Abolafia, Rishabh Singh, Manzil Zaheer +1

We present a modular neural network architecture Main that learns algorithms given a set of input-output examples. Main consists of a neural controller that interacts with a variab…

cs.PL202018 cited

Learning to Represent Programs with Property Signatures

Augustus Odena, Charles Sutton

We introduce the notion of property signatures, a representation for programs and program specifications meant for consumption by machine learning algorithms. Given a function with…

cs.LG2019

Learning to Fix Build Errors with Graph2Diff Neural Networks

Daniel Tarlow, Subhodeep Moitra, Andrew Rice +4

Professional software developers spend a significant amount of time fixing builds, but this has received little attention as a problem in automatic program repair. We present a new…