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 1721–1730 of 3073 papers

Title	Date	Tasks	Status
PI-CoF: A Bilevel Optimization Framework for Solving Active Learning Problems using Physics-Information	Feb 21, 2024	Active LearningBayesian Optimization	—Unverified
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection	Dec 13, 2022	Active LearningAdversarial Attack	—Unverified
Plex: Towards Reliability using Pretrained Large Model Extensions	Jul 15, 2022	Active LearningDecision Making	—Unverified
Point Location and Active Learning: Learning Halfspaces Almost Optimally	Apr 23, 2020	Active LearningPosition	—Unverified
Pool-Based Active Learning with Proper Topological Regions	Oct 2, 2023	Active LearningMulti-class Classification	—Unverified
Pool-based sequential active learning with multi kernels	Oct 22, 2020	Active Learning	—Unverified
Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)	Mar 17, 2020	Active LearningDiversity	—Unverified
Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification	May 18, 2018	Active LearningGeneral Classification	—Unverified
PowerEvaluationBALD: Efficient Evaluation-Oriented Deep (Bayesian) Active Learning with Stochastic Acquisition Functions	Jan 10, 2021	Active Learning	—Unverified
Practical applications of metric space magnitude and weighting vectors	Jun 24, 2020	Active LearningBoundary Detection	—Unverified

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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