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Machine learning models that take computer program source code as input typically use Natural Language Processing (NLP) techniques.
Neural program repair by jointly learning to localize and repair
Vasic, M., Kanade, A., Maniatis, P., Bieber, D., and Singh, R · 1904
Earlier work this paper cites.
Learning task-dependent distributed representations by backpropagation through structure
Goller, C. and Kuchler, A · 1996
Earlier work this paper cites.
Identifying similar code with program dependence graphs
Krinke, J · 2001
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., and Potts, C · 2013
Earlier work this paper cites.
Learning phrase representations using rnn encoder–decoder for statistical machine translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
Earlier work this paper cites.
Structured generative models of natural source code
Maddison, C. J. and Tarlow, D · 2014
Earlier work this paper cites.
Learning to discover efficient mathematical identities
Zaremba, W., Kurach, K., and Fergus, R · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
Predicting program properties from ”big code”
Raychev, V., Vechev, M., and Krause, A · 2015
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Pointer networks
Vinyals, O., Fortunato, M., and Jaitly, N · 2015
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Character-level convolutional networks for text classification
Zhang, X., Zhao, J., and LeCun, Y · 2015
Earlier work this paper cites.
Learning python code suggestion with a sparse pointer network
Bhoopchand, A., Rocktäschel, T., Barr, E. T., and Riedel, S · 2016
Earlier work this paper cites.
Pointing the unknown words
Gulcehre, C., Ahn, S., Nallapati, R., Zhou, B., and Bengio, Y · 2016
Earlier work this paper cites.
Character-aware neural language models
Kim, Y., Jernite, Y., Sontag, D., and Rush, A. M · 2016
Earlier work this paper cites.
Dynamic Entity Representation with Max-pooling Improves Machine Reading
Kobayashi, S., Tian, R., Okazaki, N., and Inui, K · 2016
Earlier work this paper cites.
Gated Graph Sequence Neural Networks
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R · 2016
Cited alongside, same era.
Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models
Luong, M.-T. and Manning, C. D · 2016
Cited alongside, same era.
Deep learning code fragments for code clone detection
White, M., Tufano, M., Vendome, C., and Poshyvanyk, D · 2016
Cited alongside, same era.
A survey of machine learning for big code and naturalness
Allamanis, M., Barr, E. T., Devanbu, P., and Sutton, C · 2017
Cited alongside, same era.
Neural Attribute Machines for Program Generation
Amodio, M., Chaudhuri, S., and Reps, T · 2017
Déjàvu: a map of code duplicates on github
Lopes, C. V., Maj, P., Martins, P. H., Saini, V., Yang, D., Zitny, J., Sajnani, H., and Vitek, J · 2017
Later among the works it cites.
Object-oriented Neural Programming (OONP) for Document Understanding
Lu, Z., Cui, H., Liu, X., Yan, Y., and Zheng, D · 2017
Later among the works it cites.
Pointer Sentinel Mixture Models
Merity, S., Xiong, C., Bradbury, J., and Socher, R · 2017
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Abstract syntax networks for code generation and semantic parsing
Rabinovich, M., Stern, M., and Klein, D · 2017
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Modeling Relational Data with Graph Convolutional Networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., Berg, R. v. d., Titov, I., and Welling, M · 2017
Later among the works it cites.
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Cited alongside, same era.
Relnet: End-to-end modeling of entities & relations
Bansal, T., Neelakantan, A., and McCallum, A · 2017
Cited alongside, same era.
Enriching word vectors with subword information
Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T · 2017
Cited alongside, same era.
Automatically generating features for learning program analysis heuristics for c-like languages
Chae, K., Oh, H., Heo, K., and Yang, H · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Cited alongside, same era.
The State of the Octoverse 2017, 2017
Github · 2017
Cited alongside, same era.
Unbounded cache model for online language modeling with open vocabulary
Grave, E., Cissé, M., and Joulin, A · 2017
Cited alongside, same era.
Are deep neural networks the best choice for modeling source code?
Hellendoorn, V. J. and Devanbu, P. T · 2017
Cited alongside, same era.
Quantum-chemical insights from deep tensor neural networks
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., and Tkatchenko, A · 2017
Later among the works it cites.
A syntactic neural model for general-purpose code generation
Yin, P. and Neubig, G · 2017
Later among the works it cites.
Learning to represent programs with graphs
Allamanis, M., Brockschmidt, M., and Khademi, M · 2018
Closest in time.
A general path-based representation for predicting program properties
Alon, U., Zilberstein, M., Levy, O., and Yahav, E · 2018
Closest in time.
Combining symbolic expressions and black-box function evaluations for training neural programs
Arabshahi, F., Singh, S., and Anandkumar, A · 2018
Closest in time.
Relational inductive biases, deep learning, and graph networks
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V. F., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., Çaglar Gülçehre, Song, F., Ballard, A. J., Gilmer, J., Dahl, G. E., Vaswani, A., Allen, K. R., Nash, C., Langston, V., Dyer, C., Heess, N., Wierstra, D., Kohli, P., Botvinick, M., Vinyals, O., Li, Y., and Pascanu, R · 2018
Closest in time.
Tree-to-tree neural networks for program translation, 2018
Chen, X., Liu, C., and Song, D · 2018
Closest in time.
Graph Memory Networks for Molecular Activity Prediction
Pham, T., Tran, T., and Venkatesh, S · 2018
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TIOBE Index for September 2018, 2018
TIOBE · 2018
Closest in time.
Generative code modeling with graphs
Brockschmidt, M., Allamanis, M., Gaunt, A. L., and Polozov, O · 2019
Closest in time.