A General Toolkit for Online Learning Approaches
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Updated
May 30, 2024 - Python
A General Toolkit for Online Learning Approaches
Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
📈 Adaptive: parallel active learning of mathematical functions
The open-source tool for building high-quality datasets and computer vision models
Explanation system for semi-supervised multi-objective optimization
Bayesian Optimization and Design of Experiments
A tool to collect triplet queries
This repository has the codes that were used in our published paper "A survey on Active learning: state-of-the-art, practical challenges and research directions"
The official implementation of A-CPD from the paper "From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning".
List of protein (enzymes and PPIs) conformations and molecular dynamics using generative artificial intelligence and deep learning
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.
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"
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