On the Generation and Removal of Speaker Adversarial Perturbation for Voice-Privacy Protection

no code implementations • 12 Dec 2024 • Chenyang Guo, Liping Chen, Zhuhai Li, Kong Aik Lee, Zhen-Hua Ling, Wu Guo

To this end, a joint training of perturbation generation and removal modules is proposed.

Attribute

FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection

no code implementations • SEMEVAL 2020 • Chenyang Guo, Xiaolong Hou, Junsong Ren, Lianxin Jiang, Yang Mo, Haiqin Yang, Jianping Shen

This paper describes the model we apply in the SemEval-2020 Task 10.

Machine Reading Comprehension

Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters

no code implementations • 20 Oct 2020 • Shaohuai Shi, Xianhao Zhou, Shutao Song, Xingyao Wang, Zilin Zhu, Xue Huang, Xinan Jiang, Feihu Zhou, Zhenyu Guo, Liqiang Xie, Rui Lan, Xianbin Ouyang, Yan Zhang, Jieqian Wei, Jing Gong, Weiliang Lin, Ping Gao, Peng Meng, Xiaomin Xu, Chenyang Guo, Bo Yang, Zhibo Chen, Yongjian Wu, Xiaowen Chu

Distributed training techniques have been widely deployed in large-scale deep neural networks (DNNs) training on dense-GPU clusters.