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Federated Pruning: Improving Neural Network Efficiency with Federated Learning


Sep 14, 2022
Rongmei Lin, Yonghui Xiao, Tien-Ju Yang, Ding Zhao, Li Xiong, Giovanni Motta, Fran├žoise Beaufays

* To appear in INTERSPEECH 2022 

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Online Model Compression for Federated Learning with Large Models


May 06, 2022
Tien-Ju Yang, Yonghui Xiao, Giovanni Motta, Fran├žoise Beaufays, Rajiv Mathews, Mingqing Chen

* Submitted to INTERSPEECH 2022 

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Partial Variable Training for Efficient On-Device Federated Learning


Oct 11, 2021
Tien-Ju Yang, Dhruv Guliani, Fran├žoise Beaufays, Giovanni Motta


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Enabling On-Device Training of Speech Recognition Models with Federated Dropout


Oct 07, 2021
Dhruv Guliani, Lillian Zhou, Changwan Ryu, Tien-Ju Yang, Harry Zhang, Yonghui Xiao, Francoise Beaufays, Giovanni Motta

* \c{opyright} 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses 

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NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization


Mar 31, 2021
Tien-Ju Yang, Yi-Lun Liao, Vivienne Sze

* Accepted by CVPR 2021 

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Design Considerations for Efficient Deep Neural Networks on Processing-in-Memory Accelerators


Dec 18, 2019
Tien-Ju Yang, Vivienne Sze

* Accepted by IEDM 2019 

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SegSort: Segmentation by Discriminative Sorting of Segments


Oct 30, 2019
Jyh-Jing Hwang, Stella X. Yu, Jianbo Shi, Maxwell D. Collins, Tien-Ju Yang, Xiao Zhang, Liang-Chieh Chen

* In ICCV 2019. Webpage & Code: https://jyhjinghwang.github.io/projects/segsort.html 

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DeeperLab: Single-Shot Image Parser


Mar 12, 2019
Tien-Ju Yang, Maxwell D. Collins, Yukun Zhu, Jyh-Jing Hwang, Ting Liu, Xiao Zhang, Vivienne Sze, George Papandreou, Liang-Chieh Chen

* 20 pages. The code of the proposed Parsing Covering metric is available at http://deeperlab.mit.edu 

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FastDepth: Fast Monocular Depth Estimation on Embedded Systems


Mar 08, 2019
Diana Wofk, Fangchang Ma, Tien-Ju Yang, Sertac Karaman, Vivienne Sze

* Accepted for presentation at ICRA 2019. 8 pages, 6 figures, 7 tables 

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