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Parameter-Efficient Fine-Tuning (PEFT) is increasingly recognized as an effective method in speech processing.
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Hanxiao Liu, Karen Simonyan, and Yiming Yang, · 2018
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Alex Nichol, Joshua Achiam, and John Schulman, · 2018
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Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly, · 2019
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Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, and Iryna Gurevych, · 2020
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“Hubert: Self-supervised speech representation learning by masked prediction of hidden units,”
Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, and Abdelrahman Mohamed, · 2021
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“Lora: Low-rank adaptation of large language models,”
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen, · 2021
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“Layer-wise analysis of a self-supervised speech representation model,”
Ankita Pasad, Ju-Chieh Chou, and Karen Livescu, · 2021
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“A comprehensive survey of neural architecture search: Challenges and solutions,”
Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang, · 2021
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“Warped ensembles: A novel technique for improving ctc based end-to-end speech recognition,”
Kiran Praveen, Hardik Sailor, and Abhishek Pandey, · 2021
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“Superb: Speech processing universal performance benchmark,”
Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang, Cheng-I Jeff Lai, Kushal Lakhotia, Yist Y Lin, Andy T Liu, Jiatong Shi, Xuankai Chang, Guan-Ting Lin, et al., · 2021
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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, · 2022
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“Self-supervised speech representation learning: A review,”
Abdelrahman Mohamed, Hung-yi Lee, Lasse Borgholt, Jakob D Havtorn, Joakim Edin, Christian Igel, Katrin Kirchhoff, Shang-Wen Li, Karen Livescu, Lars Maaløe, et al., · 2022
“Chapter: Exploiting convolutional neural network adapters for self-supervised speech models,”
Zih-Ching Chen, Yu-Shun Sung, and Hung-yi Lee, · 2022
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Nafise Sadat Moosavi, Quentin Delfosse, Kristian Kersting, and Iryna Gurevych, · 2022
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“Parameter-efficient fine-tuning design spaces,”
Jiaao Chen, Aston Zhang, Xingjian Shi, Mu Li, Alex Smola, and Diyi Yang, · 2023
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“Exploring efficient-tuning methods in self-supervised speech models,”
Zih-Ching Chen, Chin-Lun Fu, Chih-Ying Liu, Shang-Wen Daniel Li, and Hung-yi Lee, · 2023
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“Autopeft: Automatic configuration search for parameter-efficient fine-tuning,”
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N. Ding, Y. Qin, G. Yang, F. Wei, Z. Yang, Y. Su, S. Hu, Y. Chen, C.-M. Chan, W. Chen, et al., · 2022
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“Adapterbias: Parameter-efficient token-dependent representation shift for adapters in nlp tasks,”
Chin-Lun Fu, Zih-Ching Chen, Yun-Ru Lee, and Hung-yi Lee, · 2022
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“An adapter based pre-training for efficient and scalable self-supervised speech representation learning,”
Samuel Kessler, Bethan Thomas, and Salah Karout, · 2022
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“An exploration of prompt tuning on generative spoken language model for speech processing tasks,”
Kai-Wei Chang, Wei-Cheng Tseng, Shang-Wen Li, and Hung-yi Lee, · 2022
Cited alongside, same era.
Han Zhou, Xingchen Wan, Ivan Vulić, and Anna Korhonen, · 2023
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“Findadaptnet: Find and insert adapters by learned layer importance,”
Junwei Huang, Karthik Ganesan, Soumi Maiti, Young Min Kim, Xuankai Chang, Paul Liang, and Shinji Watanabe, · 2023
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“Hydra: Multi-head low-rank adaptation for parameter efficient fine-tuning,” 2023
Sanghyeon Kim, Hyunmo Yang, Younghyun Kim, Youngjoon Hong, and Eunbyung Park, · 2023
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“Minisuperb: Lightweight benchmark for self-supervised speech models,”
Yu-Hsiang Wang, Huang-Yu Chen, Kai-Wei Chang, Winston Hsu, and Hung-yi Lee, · 2023
Later among the works it cites.