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Learning neural program embeddings is key to utilizing deep neural networks in program languages research --- precise and efficient program representations enable the application of deep models to a wide range of program analysis tasks.
A Neural Probabilistic Language Model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. 2003 · 2003
Earlier work this paper cites.
Domain Adaptation for Large-scale Sentiment Classification: A Deep Learning Approach. In
Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011 · 2011
Earlier work this paper cites.
Efficient estimation of word representations in vector space
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Earlier work this paper cites.
Multiple object recognition with visual attention
Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu. 2014 · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Fast and robust neural network joint models for statistical machine translation. In
Jacob Devlin, Rabih Zbib, Zhongqiang Huang, Thomas Lamar, Richard Schwartz, and John Makhoul. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Distributed Representations of Sentences and Documents. In
Quoc Le and Tomas Mikolov. 2014 · 2014
Earlier work this paper cites.
Recurrent models of visual attention. In
Volodymyr Mnih, Nicolas Heess, Alex Graves, et al · 2014
Cited alongside, same era.
Attention-based models for speech recognition. In
Jan K Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015 · 2015
Cited alongside, same era.
End-to-end attention-based large vocabulary speech recognition. In
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Cited alongside, same era.
Convolutional neural networks over tree structures for programming language processing. In
Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. 2016 · 2016
Cited alongside, same era.
Sk_P: A Neural Program Corrector for MOOCs. In
Attention is all you need. In
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Later among the works it cites.
Dynamic Neural Program Embedding for Program Repair
Ke Wang, Rishabh Singh, and Zhendong Su. 2017 · 2017
Later among the works it cites.
code2seq: Generating sequences from structured representations of code
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Later among the works it cites.
Code Vectors: Understanding Programs Through Embedded Abstracted Symbolic Traces. In
Jordan Henkel, Shuvendu K. Lahiri, Ben Liblit, and Thomas Reps. 2018 · 2018
Later among the works it cites.
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