List of protein (enzymes and PPIs) conformations and molecular dynamics using generative artificial intelligence and deep learning

Bayesian Optimization and Design of Experiments

The open-source tool for building high-quality datasets and computer vision models

Explanation system for semi-supervised multi-objective optimization

INCEpTION provides a semantic annotation platform offering intelligent annotation assistance and knowledge management.

Experimental design and (multi-objective) bayesian optimization.

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.

Bayesian active learning library for research and industrial usecases.

Active learning for systematic reviews

MONAI Label is an intelligent open source image labeling and learning tool.

Honegumi (骨組み) is an interactive "skeleton code" generator for API tutorials focusing on optimization packages.

A simple interface to inspect, improve and add concepts to biomedical NER+L -> MedCAT.

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Official PyTorch implementation of the ICML 2024 paper "Hyperbolic Active Learning for Semantic Segmentation under Domain Shift"

Smarter Manual Annotation for Resource-constrained collection of Training data

This is a repository for CS4ML. It is a general framework for active learning in regression problems. It approximates a target function arising from general types of data, rather than pointwise samples.

[MedIA] Accompanying paper list and source code for survey "A comprehensive survey on deep active learning in medical image analysis"

Active Learning for Text Classification in Python

NOVA is a tool for annotating and analyzing behaviours in social interactions. It supports Annotators using Machine Learning already during the coding process. Further it features both, discrete labels and continuous scores and a visuzalization of streams recorded with the SSI Framework.