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Large ASR models can inadvertently leak sensitive information, which can be mitigated by formal privacy measures like differential privacy (DP).
Random projection in dimensionality reduction: applications to image and text data
Ella Bingham and Heikki Mannila · 2001
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber · 2006
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam D. Smith, and Abhradeep Thakurta · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Librispeech: an asr corpus based on public domain audio books
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Parallel wavenet: Fast high-fidelity speech synthesis
Aaron Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George Driessche, Edward Lockhart, Luis Cobo, Florian Stimberg, et al · 2018
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Conformer: Convolution-augmented transformer for speech recognition
Anmol Gulati, James Qin, Chung-Cheng Chiu, Niki Parmar, Yu Zhang, Jiahui Yu, Wei Han, Shibo Wang, Zhengdong Zhang, Yonghui Wu, et al · 2020
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Practical and private (deep) learning without sampling or shuffling
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu · 2021
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Do not let privacy overbill utility: Gradient embedding perturbation for private learning
Da Yu, Huishuai Zhang, Wei Chen, and Tie-Yan Liu · 2021
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Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al · 2021
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Large scale private learning via low-rank reparametrization
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu · 2021
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When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A Inan, Janardhan Kulkarni, YinTat Lee, and Abhradeep Guha Thakurta · 2022
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https://github.com/google/paxml, 2022
PAXML · 2022
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Google usm: Scaling automatic speech recognition beyond 100 languages
Yu Zhang, Wei Han, James Qin, Yongqiang Wang, Ankur Bapna, Zhehuai Chen, Nanxin Chen, Bo Li, Vera Axelrod, Gary Wang, et al · 2023
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Robust speech recognition via large-scale weak supervision
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever · 2023
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Noise masking attacks and defenses for pretrained speech models
Matthew Jagielski, Om Thakkar, and Lun. Wang · 2023
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Modular domain adaptation for conformer-based streaming asr
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Compacter: Efficient low-rank hypercomplex adapter layers
Rabeeh Karimi Mahabadi, James Henderson, and Sebastian Ruder · 2021
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Practical and private (deep) learning without sampling or shuffling
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu · 2021
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Extracting targeted training data from asr models, and how to mitigate it
Ehsan Amid, Om Thakkar, Arun Narayanan, Rajiv Mathews, and Françoise Beaufays · 2022
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When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori B. Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, and Abhradeep Guha Thakurta · 2022
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Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
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Differentially private bias-term only fine-tuning of foundation models
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, and George Karypis · 2022
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben Zaken, Shauli Ravfogel, and Yoav Goldberg · 2022
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Qiujia Li, Bo Li, Dongseong Hwang, Tara N Sainath, and Pedro M Mengibar · 2023
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Efficient adapters for giant speech models
Nanxin Chen, Izhak Shafran, Yu Zhang, Chung-Cheng Chiu, Hagen Soltau, James Qin, and Yonghui Wu · 2023
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How to dp-fy ml: A practical guide to machine learning with differential privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Guha Thakurta · 2023
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al · 2024
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https://openai.com/index/hello-gpt-4o, 2024
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Unintended memorization in large asr models, and how to mitigate it
Lun Wang, Om Thakkar, and Rajiv Mathews · 2024
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Pre-trained encoders in self-supervised learning improve secure and privacy-preserving supervised learning
Hongbin Liu, Wenjie Qu, Jinyuan Jia, and Neil Zhenqiang Gong · 2024
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