no code implementations • 29 Sep 2023 • Lillian Zhou, Yuxin Ding, Mingqing Chen, Harry Zhang, Rohit Prabhavalkar, Dhruv Guliani, Giovanni Motta, Rajiv Mathews
Automatic speech recognition (ASR) models are typically trained on large datasets of transcribed speech.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 5 Aug 2022 • Sandy Ritchie, You-Chi Cheng, Mingqing Chen, Rajiv Mathews, Daan van Esch, Bo Li, Khe Chai Sim
Almost none of the 2, 000+ languages spoken in Africa have widely available automatic speech recognition systems, and the required data is also only available for a few languages.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 2 Jul 2022 • Theresa Breiner, Swaroop Ramaswamy, Ehsan Variani, Shefali Garg, Rajiv Mathews, Khe Chai Sim, Kilol Gupta, Mingqing Chen, Lara McConnaughey
We experiment on a user-clustered LibriSpeech corpus, supplemented with personalized text-only data for each user from Project Gutenberg.
no code implementations • 6 May 2022 • Tien-Ju Yang, Yonghui Xiao, Giovanni Motta, Françoise Beaufays, Rajiv Mathews, Mingqing Chen
This paper addresses the challenges of training large neural network models under federated learning settings: high on-device memory usage and communication cost.
no code implementations • FL4NLP (ACL) 2022 • Jae Hun Ro, Theresa Breiner, Lara McConnaughey, Mingqing Chen, Ananda Theertha Suresh, Shankar Kumar, Rajiv Mathews
Most studies in cross-device federated learning focus on small models, due to the server-client communication and on-device computation bottlenecks.
no code implementations • 16 Feb 2022 • Hao Zhang, You-Chi Cheng, Shankar Kumar, W. Ronny Huang, Mingqing Chen, Rajiv Mathews
Capitalization normalization (truecasing) is the task of restoring the correct case (uppercase or lowercase) of noisy text.
no code implementations • 6 Oct 2021 • Hakim Sidahmed, Zheng Xu, Ankush Garg, Yuan Cao, Mingqing Chen
Through extensive experiments, we empirically show that Federated learning of Partially Trainable neural networks (FedPT) can result in superior communication-accuracy trade-offs, with up to $46\times$ reduction in communication cost, at a small accuracy cost.
no code implementations • ICLR 2022 • Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konečný, Andrew Hard, Tom Goldstein
Federated learning has been deployed to train machine learning models from decentralized client data on mobile devices in practice.
no code implementations • 26 Aug 2021 • Hao Zhang, You-Chi Cheng, Shankar Kumar, Mingqing Chen, Rajiv Mathews
Truecasing is the task of restoring the correct case (uppercase or lowercase) of noisy text generated either by an automatic system for speech recognition or machine translation or by humans.
no code implementations • 6 Apr 2021 • Jae Ro, Mingqing Chen, Rajiv Mathews, Mehryar Mohri, Ananda Theertha Suresh
We propose a communication-efficient distributed algorithm called Agnostic Federated Averaging (or AgnosticFedAvg) to minimize the domain-agnostic objective proposed in Mohri et al. (2019), which is amenable to other private mechanisms such as secure aggregation.
3 code implementations • ICLR 2020 • Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Aguera y Arcas
To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact.
no code implementations • CONLL 2019 • Mingqing Chen, Ananda Theertha Suresh, Rajiv Mathews, Adeline Wong, Cyril Allauzen, Françoise Beaufays, Michael Riley
The n-gram language models trained with federated learning are compared to n-grams trained with traditional server-based algorithms using A/B tests on tens of millions of users of virtual keyboard.
no code implementations • 26 Mar 2019 • Mingqing Chen, Rajiv Mathews, Tom Ouyang, Françoise Beaufays
We demonstrate that a character-level recurrent neural network is able to learn out-of-vocabulary (OOV) words under federated learning settings, for the purpose of expanding the vocabulary of a virtual keyboard for smartphones without exporting sensitive text to servers.
no code implementations • 25 Jul 2017 • Dong Yang, Daguang Xu, S. Kevin Zhou, Bogdan Georgescu, Mingqing Chen, Sasa Grbic, Dimitris Metaxas, Dorin Comaniciu
Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment.
no code implementations • 17 May 2017 • Dong Yang, Tao Xiong, Daguang Xu, Qiangui Huang, David Liu, S. Kevin Zhou, Zhoubing Xu, Jin-Hyeong Park, Mingqing Chen, Trac. D. Tran, Sang Peter Chin, Dimitris Metaxas, Dorin Comaniciu
In this paper, we propose an automatic and fast algorithm to localize and label the vertebra centroids in 3D CT volumes.