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Recent advances in deep learning have drastically improved performance on many Natural Language Understanding (NLU) tasks.
“Differentially private learning with adaptive clipping,”
O. Thakkar, G. Andrew, and H. B. McMahan, · 1905
Earlier work this paper cites.
“Evaluating differentially private machine learning in practice,”
B. Jayaraman and D. Evans, · 1912
Earlier work this paper cites.
“The atis spoken language systems pilot corpus,”
C. Hemphill, J. Godfrey, and G. Doddington, · 1990
Earlier work this paper cites.
“Calibrating noise to sensitivity in private data analysis,”
C. Dwork, F. McSherry, K. Nissim, and A. Smith, · 2006
Earlier work this paper cites.
“Deep learning with differential privacy,”
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, · 2016
Earlier work this paper cites.
“End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF,”
X. Ma and E. Hovy, · 2016
Earlier work this paper cites.
“Membership inference attacks against machine learning models,”
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, · 2017
Cited alongside, same era.
Preventing Overfitting in Deep Learning Using Differential Privacy
A. A. H. Khatri, · 2017
Cited alongside, same era.
“Membership inference attack against differentially private deep learning model,”
A. Rahman, T. Rahman, R. Laganière, and N. Mohammed, · 2018
Cited alongside, same era.
“A re-ranker scheme for integrating large scale nlu models,”
C. Su, R. Gupta, S. Ananthakrishnan, and S. Matsoukas, · 2018
Cited alongside, same era.
“Advances in pre-training distributed word representations,”
T. Mikolov, E. Grave, P. Bojanowski, C. Puhrsch, and A. Joulin, · 2018
Cited alongside, same era.
“Bert with history answer embedding for conversational question answering,”
C. Qu, L. Yang, M. Qiu, W. B. Croft, Y. Zhang, and M. Iyyer, · 2019
“Pooled contextualized embeddings for named entity recognition,”
A. Akbik, T. Bergmann, and R. Vollgraf, · 2019
Later among the works it cites.
“Amplification by shuffling: From local to central differential privacy via anonymity,”
Ú. Erlingsson, V. Feldman, I. Mironov, A. Raghunathan, K. Talwar, and A. Thakurta, · 2019
Later among the works it cites.
“Differential privacy defenses and sampling attacks for membership inference,”
S. Rahimian, T. Orekondy, and M. Fritz, · 2019
Later among the works it cites.
“Differentially private model publishing for deep learning,”
L. Yu, L. Liu, C. Pu, M. E. Gursoy, and S. Truex, · 2019
Later among the works it cites.
“Benchmarking natural language understanding services for building conversational agents,”
P. Swietojanski X. Liu, A. Eshghi and V. Rieser, · 2019
Later among the works it cites.
“Understanding gradient clipping in private sgd: A geometric perspective,”
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Cited alongside, same era.
“Federated learning of deep networks using model averaging,”
H. B. McMahan, E. Moore, D. Ramage, and B. Agüera y Arcas,
Cited in the paper.
“Learning differentially private recurrent language models,”
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang,
Cited in the paper.
“Differentially private generative adversarial network,”
L. Xie, K. Lin, S. Wang, F. Wang, and J. Zhou,
Cited in the paper.
“cpsgd: Communication-efficient and differentially-private distributed sgd,”
N. Agarwal, A. T. Suresh, F. Yu, S. Kumar, and H. B. McMahan,
Cited in the paper.
“Towards demystifying membership inference attacks,”
S. Truex, L. Liu, M. E. Gursoy, L. Yu, and W. Wei,
Cited in the paper.
A. Salem, Y. Zhang, M. Humbert, M. Fritz, and M. Backes,
Cited in the paper.
X. Chen, S. Z. Wu, and M. Hong, · 2020
Later among the works it cites.