Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Yao, Houpu"'
As there is a growing interest in utilizing data across multiple resources to build better machine learning models, many vertically federated learning algorithms have been proposed to preserve the data privacy of the participating organizations. Howe
Externí odkaz:
http://arxiv.org/abs/2201.10761
Autor:
Liu, Bo, Tan, Chaowei, Wang, Jiazhou, Zeng, Tao, Shan, Huasong, Yao, Houpu, Huang, Heng, Dai, Peng, Bo, Liefeng, Chen, Yanqing
In this paper, we present Fedlearn-Algo, an open-source privacy preserving machine learning platform. We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms. As the first batch of no
Externí odkaz:
http://arxiv.org/abs/2107.04129
An innovative physics-guided learning algorithm for predicting the mechanical response of materials and structures is proposed in this paper. The key concept of the proposed study is based on the fact that physics models are governed by Partial Diffe
Externí odkaz:
http://arxiv.org/abs/2002.01893
We demonstrate in this paper that a generative model can be designed to perform classification tasks under challenging settings, including adversarial attacks and input distribution shifts. Specifically, we propose a conditional variational autoencod
Externí odkaz:
http://arxiv.org/abs/1902.03361
Special high-end sensors with expensive hardware are usually needed to measure shock signals with high accuracy. In this paper, we show that cheap low-end sensors calibrated by deep neural networks are also capable to measure high-g shocks accurately
Externí odkaz:
http://arxiv.org/abs/1902.02829
The vulnerability of neural networks under adversarial attacks has raised serious concerns and motivated extensive research. It has been shown that both neural networks and adversarial attacks against them can be sensitive to input transformations su
Externí odkaz:
http://arxiv.org/abs/1901.11188
Fracture pattern prediction with random microstructure using a physics-informed deep neural networks
Publikováno v:
In Engineering Fracture Mechanics 1 June 2022 268
Publikováno v:
In Sensors and Actuators: A. Physical 1 September 2021 328
Publikováno v:
In Measurement September 2021 182
Publikováno v:
In Engineering Applications of Artificial Intelligence November 2020 96