Pool-based active learning in Python

UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.

Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.

Code and website for DAL (Discriminative Active Learning) - a new active learning algorithm for neural networks in the batch setting. For the blog:

SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream

ECHO is a semi-supervised framework for classifying evolving data streams based on our previous approach SAND. The most expensive module of SAND is the change detection module, which has cubic time complexity. ECHO uses dynamic programming to reduce the time complexity. Moreover, ECHO has a maximum allowable sliding window size. If there is no c…

Spring 2021 - Automation of Scientific Research - course project

PHD Thesis at CERFACS: Uncertainty Quantification for High Dimensional Problems

Open Active Learning for Deep Models

This library proposes a plug-in approach to active learning utilizing bagging techniques. Bagging, or bootstrap aggregating, is an ensemble learning method designed to improve the stability and accuracy of machine learning algorithms.