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 101–110 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
A comprehensive survey on deep active learning in medical image analysis	Oct 22, 2023	Active LearningInformativeness	CodeCode Available	1	5
Active Learning from the Web	Oct 15, 2022	Active LearningInformation Retrieval	CodeCode Available	1	5
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)	Oct 6, 2021	Active Learning	CodeCode Available	1	5
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes	Dec 12, 2020	Active LearningBinary Classification	CodeCode Available	1	5
Active Learning Meets Optimized Item Selection	Nov 22, 2021	Active LearningClustering	CodeCode Available	1	5
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings	Oct 16, 2020	Active LearningClustering	CodeCode Available	1	5
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets	Feb 6, 2022	Active Learning	CodeCode Available	1	5
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning	Apr 16, 2021	Active LearningNatural Language Understanding	CodeCode Available	1	5
Bayesian Optimization with Conformal Prediction Sets	Oct 22, 2022	Active LearningBayesian Optimization	CodeCode Available	1	5
Active Anomaly Detection via Ensembles	Sep 17, 2018	Active LearningAnomaly Detection	CodeCode Available	1	5

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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