Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 161–170 of 3073 papers

Title	Date	Tasks	Status	Hype
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction	Jul 7, 2020	Active LearningGraph Neural Network	CodeCode Available	1
Active learning based generative design for the discovery of wide bandgap materials	Feb 28, 2021	Active LearningBand Gap	CodeCode Available	1
Open Source Software for Efficient and Transparent Reviews	Jun 22, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	1
AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active Learning	Sep 11, 2021	Active LearningMissing Labels	CodeCode Available	1
A Survey of Dataset Refinement for Problems in Computer Vision Datasets	Oct 21, 2022	Active Learning	CodeCode Available	1
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language Models	Dec 16, 2021	Active LearningLanguage Modeling	CodeCode Available	1
Bayesian active learning for production, a systematic study and a reusable library	Jun 17, 2020	Active Learning	CodeCode Available	1
Bayesian Active Learning with Fully Bayesian Gaussian Processes	May 20, 2022	Active LearningGaussian Processes	CodeCode Available	1
Bayesian Model-Agnostic Meta-Learning	Jun 11, 2018	Active Learningimage-classification	CodeCode Available	1
Active learning for medical image segmentation with stochastic batches	Jan 18, 2023	Active LearningDiversity	CodeCode Available	1

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TypiClust	Accuracy	93.2	—	Unverified
2	PT4AL	Accuracy	93.1	—	Unverified
3	Learning loss	Accuracy	91.01	—	Unverified
4	CoreGCN	Accuracy	90.7	—	Unverified
5	Core-set	Accuracy	89.92	—	Unverified
6	Random Baseline (Resnet18)	Accuracy	88.45	—	Unverified
7	Random Baseline (VGG16)	Accuracy	85.09	—	Unverified