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Deep learning has demonstrated great abilities in various code generation tasks.
Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security . 308–318
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Earlier work this paper cites.
Probabilistic model for code with decision trees
Veselin Raychev, Pavol Bielik, and Martin T. Vechev. 2016 · 2016
Earlier work this paper cites.
Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2017 · 2017
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Understanding black-box predictions via influence functions. In International Conference on Machine Learning . PMLR, 1885–1894
Pang Wei Koh and Percy Liang. 2017 · 2017
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Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov. 2019 · 2019
Earlier work this paper cites.
Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2020
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Pmc: A privacy-preserving deep learning model customization framework for edge computing
Bingyan Liu, Yuanchun Li, Yunxin Liu, Yao Guo, and Xiangqun Chen. 2020 · 2020
Cited alongside, same era.
Privacy risks of general-purpose language models. In 2020 IEEE Symposium on Security and Privacy (SP) . IEEE, 1314–1331
Xudong Pan, Mi Zhang, Shouling Ji, and Min Yang. 2020 · 2020
Cited alongside, same era.
Dynamic slicing for deep neural networks. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 838–850
Ziqi Zhang, Yuanchun Li, Yao Guo, Xiangqun Chen, and Yunxin Liu. 2020 · 2020
Cited alongside, same era.
GitHub, Copilot and the Copyright Around AI
2021 · 2021
Cited alongside, same era.
Unified Pre-training for Program Understanding and Generation
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
Copyright in Generative Deep Learning
Giorgio Franceschelli and Mirco Musolesi. 2021 · 2021
Later among the works it cites.
Training data leakage analysis in language models
Huseyin A Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, and Robert Sim. 2021 · 2021
Later among the works it cites.
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al · 2021
Later among the works it cites.
An Empirical Cybersecurity Evaluation of GitHub Copilot’s Code Contributions
Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri. 2021 · 2021
Later among the works it cites.
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Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harri Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al
Cited in the paper.
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021 · 2021
Later among the works it cites.