no code implementations • EAMT 2020 • Hao Yang, Minghan Wang, Ning Xie, Ying Qin, Yao Deng
Compared with the commonly used NuQE baseline, BAL-QE achieves 47% (En-Ru) and 75% (En-De) of performance promotions.
no code implementations • EAMT 2020 • Minghan Wang, Hao Yang, Ying Qin, Shiliang Sun, Yao Deng
We propose a unified multilingual model for humor detection which can be trained under a transfer learning framework.
no code implementations • 2 Apr 2024 • Xiang Xiang, Zihan Zhang, Jing Ma, Yao Deng
Parkinson's Disease (PD) is the second most common neurodegenerative disorder.
no code implementations • 25 May 2023 • Linfeng Liang, Yao Deng, Yang Zhang, Jianchao Lu, Chen Wang, Quanzheng Sheng, Xi Zheng
Discrepancies in decision-making between Autonomous Driving Systems (ADS) and human drivers underscore the need for intuitive human gaze predictors to bridge this gap, thereby improving user trust and experience.
no code implementations • 5 Apr 2021 • Yao Deng, Tiehua Zhang, Guannan Lou, Xi Zheng, Jiong Jin, Qing-Long Han
The rapid development of artificial intelligence, especially deep learning technology, has advanced autonomous driving systems (ADSs) by providing precise control decisions to counterpart almost any driving event, spanning from anti-fatigue safe driving to intelligent route planning.
no code implementations • 12 Mar 2021 • Chenhao Xu, Jiaqi Ge, Yong Li, Yao Deng, Longxiang Gao, Mengshi Zhang, Yong Xiang, Xi Zheng
Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy.
no code implementations • WS 2020 • Minghan Wang, Hao Yang, Yao Deng, Ying Qin, Lizhi Lei, Daimeng Wei, Hengchao Shang, Ning Xie, Xiaochun Li, Jiaxian Guo
The paper presents details of our system in the IWSLT Video Speech Translation evaluation.
1 code implementation • 6 Feb 2020 • Yao Deng, Xi Zheng, Tianyi Zhang, Chen Chen, Guannan Lou, Miryung Kim
We derive several implications for system and middleware builders: (1) when adding a defense component against adversarial attacks, it is important to deploy multiple defense methods in tandem to achieve a good coverage of various attacks, (2) a blackbox attack is much less effective compared with a white-box attack, implying that it is important to keep model details (e. g., model architecture, hyperparameters) confidential via model obfuscation, and (3) driving models with a complex architecture are preferred if computing resources permit as they are more resilient to adversarial attacks than simple models.