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Natural language processing has improved tremendously after the success of word embedding techniques such as word2vec.
Liii. on lines and planes of closest fit to systems of points in space
Karl Pearson · 1901
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Deep api learning
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Mapping api elements for code migration with vector representations
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Semantic code repair using neuro-symbolic transformation networks
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Neural network-based graph embedding for cross-platform binary code similarity detection
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code2seq: Generating sequences from structured representations of code
Uri Alon, Omer Levy, and Eran Yahav · 2018
Code vectors: understanding programs through embedded abstracted symbolic traces
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Deepbugs: a learning approach to name-based bug detection
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Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, and Alexander L Gaunt · 2018
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Neural-augmented static analysis of android communication
Jinman Zhao, Aws Albarghouthi, Vaibhav Rastogi, Somesh Jha, and Damien Octeau · 2018
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Path-based function embedding and its application to specification mining
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Automated software vulnerability detection with machine learning
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Learning to represent programs with graphs
Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi
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Neural machine translation inspired binary code similarity comparison beyond function pairs
Fei Zuo, Xiaopeng Li, Zhexin Zhang, Patrick Young, Lannan Luo, and Qiang Zeng · 2018
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code2vec: Learning distributed representations of code
Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav · 2019
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user2code2vec: Embeddings for profiling students based on distributional representations of source code
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Learning-based recursive aggregation of abstract syntax trees for code clone detection
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