no code implementations • 19 Nov 2021 • Zhigang Ye, Chen Chen, Ruihuan Liu, Kai Wu, Zhaohong Bie, Guannan Lou, Wei Gu, Yubo Yuan
Enhancing restoration capabilities of distribution systems is one of the main strategies for resilient power systems to cope with extreme events.
1 code implementation • 12 Nov 2021 • Guannan Lou, Yuze Liu, Tiehua Zhang, Xi Zheng
We present a spatial-temporal federated learning framework for graph neural networks, namely STFL.
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.
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.