Abstract: The article proposes a new method for teaching private classifiers, as well as a way to aggregate their forecasts as part of a committee. The training is based on the hypothesis of iterative ...

In the PyRBP, we integrate several machine learning classifiers from sklearn and implement several classical deep learning models for users to perform performance tests, for which we provide two ...

Compare metrics such as f1 score and confusion matrices for your machine learning models and through voting or stacking them, then predict on test data with your choice of voting or stacking! This ...

Abstract: Adversarial examples that can fool neural network classifiers have attracted much attention. Existing approaches to detect adversarial examples leverage a supervised scheme in generating ...