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Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax.
A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
Earlier work this paper cites.
Program synthesis by sketching
Armando Solar-Lezama · 2008
Earlier work this paper cites.
A few billion lines of code later: using static analysis to find bugs in the real world
Al Bessey, Ken Block, Ben Chelf, Andy Chou, Bryan Fulton, Seth Hallem, Charles Henri-Gros, Asya Kamsky, Scott McPeak, and Dawson Engler · 2010
Earlier work this paper cites.
On the naturalness of software
Abram Hindle, Earl T Barr, Zhendong Su, Mark Gabel, and Premkumar Devanbu · 2012
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
Earlier work this paper cites.
Learning natural coding conventions
Miltiadis Allamanis, Earl T Barr, Christian Bird, and Charles Sutton · 2014
Earlier work this paper cites.
On the properties of neural machine translation: Encoder–decoder approaches
Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
Earlier work this paper cites.
Structured generative models of natural source code
Chris J Maddison and Daniel Tarlow · 2014
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
Earlier work this paper cites.
Code completion with statistical language models
Veselin Raychev, Martin Vechev, and Eran Yahav · 2014
Earlier work this paper cites.
Suggesting accurate method and class names
Miltiadis Allamanis, Earl T Barr, Christian Bird, and Charles Sutton · 2015
Cited alongside, same era.
Automated software transplantation
Earl T Barr, Mark Harman, Yue Jia, Alexandru Marginean, and Justyna Petke · 2015
Cited alongside, same era.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
Cited alongside, same era.
Predicting program properties from Big Code
Veselin Raychev, Martin Vechev, and Andreas Krause · 2015
Cited alongside, same era.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
Cited alongside, same era.
A convolutional attention network for extreme summarization of source code
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Later among the works it cites.
Probabilistic model for code with decision trees
Veselin Raychev, Pavol Bielik, and Martin Vechev · 2016
Later among the works it cites.
A survey of machine learning for big code and naturalness
Miltiadis Allamanis, Earl T Barr, Premkumar Devanbu, and Charles Sutton · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Miltiadis Allamanis, Hao Peng, and Charles Sutton · 2016
Cited alongside, same era.
Learning Python code suggestion with a sparse pointer network
Avishkar Bhoopchand, Tim Rocktäschel, Earl Barr, and Sebastian Riedel · 2016
Cited alongside, same era.
Statistical deobfuscation of android applications
Benjamin Bichsel, Veselin Raychev, Petar Tsankov, and Martin Vechev · 2016
Cited alongside, same era.
PHOG: probabilistic model for code
Pavol Bielik, Veselin Raychev, and Martin Vechev · 2016
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
Cited alongside, same era.
Encoding sentences with graph convolutional networks for semantic role labeling
Diego Marcheggiani and Ivan Titov · 2017
Closest in time.
Detecting argument selection defects
Andrew Rice, Edward Aftandilian, Ciera Jaspan, Emily Johnston, Michael Pradel, and Yulissa Arroyo-Paredes · 2017
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Modeling relational data with graph convolutional network
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2017
Closest in time.
Premise selection for theorem proving by deep graph embedding
Mingzhe Wang, Yihe Tang, Jian Wang, and Jia Deng · 2017
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