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Found 6 papers, 1 papers with code
An Efficient and Robust System for Vertically Federated Random Forest
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.
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms.
FEA-Net: A Physics-guided Data-driven Model for Efficient Mechanical Response Prediction
Based on this, a special type of deep convolutional neural network (DCNN) is proposed that takes advantage of our prior knowledge in physics to build data-driven models whose architectures are of physics meaning.
Image Decomposition and Classification through a Generative Model
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.
Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration
The results show that the proposed network is capable to map low-end shock signals to its high-end counterparts with satisfactory accuracy.
Augmenting Model Robustness with Transformation-Invariant Attacks
The vulnerability of neural networks under adversarial attacks has raised serious concerns and motivated extensive research.
