no code implementations • 5 Nov 2024 • Xuewei Cheng, Ke Huang, Shujie Ma
Recurrent Neural Networks (RNNs) have achieved great success in the prediction of sequential data.
no code implementations • 4 Jul 2023 • Xuewei Cheng, Ke Huang, Yi Zou, Shujie Ma
Deep neural networks have played an important role in automatic sleep stage classification because of their strong representation and in-model feature transformation abilities.
no code implementations • 23 Dec 2022 • Runmin Cong, Ke Huang, Jianjun Lei, Yao Zhao, Qingming Huang, Sam Kwong
Salient object detection (SOD) aims to determine the most visually attractive objects in an image.
no code implementations • ICCV 2023 • Zahra Ghodsi, Mojan Javaheripi, Nojan Sheybani, Xinqiao Zhang, Ke Huang, Farinaz Koushanfar
However, keeping the individual updates private allows malicious users to perform Byzantine attacks and degrade the accuracy without being detected.
no code implementations • 12 Apr 2022 • Huili Chen, Xinqiao Zhang, Ke Huang, Farinaz Koushanfar
This paper proposes AdaTest, a novel adaptive test pattern generation framework for efficient and reliable Hardware Trojan (HT) detection.
no code implementations • 8 Apr 2022 • Xinqiao Zhang, Huili Chen, Ke Huang, Farinaz Koushanfar
Deep Neural Networks (DNNs) have demonstrated unprecedented performance across various fields such as medical diagnosis and autonomous driving.
1 code implementation • 5 Apr 2022 • Paarth Neekhara, Shehzeen Hussain, Xinqiao Zhang, Ke Huang, Julian McAuley, Farinaz Koushanfar
We demonstrate that FaceSigns can embed a 128 bit secret as an imperceptible image watermark that can be recovered with a high bit recovery accuracy at several compression levels, while being non-recoverable when unseen Deepfake manipulations are applied.
no code implementations • 16 Feb 2021 • Ke Huang, Yu Wang, Xiao Li
Recently a class of quantum systems exhibiting weak ergodicity breaking has attracted much attention.
Disordered Systems and Neural Networks Statistical Mechanics
no code implementations • 21 May 2018 • Mohammad Ghasemzadeh, Fang Lin, Bita Darvish Rouhani, Farinaz Koushanfar, Ke Huang
The success of deep learning models is heavily tied to the use of massive amount of labeled data and excessively long training time.