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 711–720 of 3073 papers

Title	Date	Tasks	Status	Score
Bidirectional Uncertainty-Based Active Learning for Open Set Annotation	Feb 23, 2024	Active Learning	CodeCode Available	5
Active Learning for Non-Parametric Regression Using Purely Random Trees	Dec 1, 2018	Active LearningBinary Classification	CodeCode Available	5
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions	Oct 5, 2022	Active LearningAutonomous Driving	CodeCode Available	5
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions	Mar 29, 2017	Active LearningBayesian Inference	CodeCode Available	5
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise	Jul 8, 2024	Active LearningClassification	CodeCode Available	5
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted Networks	May 29, 2025	Active Learning	CodeCode Available	5
Bayesian Semi-supervised Learning with Graph Gaussian Processes	Sep 12, 2018	Active LearningGaussian Processes	CodeCode Available	5
FOIT: Fast Online Instance Transfer for Improved EEG Emotion Recognition	Feb 1, 2021	Active LearningBrain Computer Interface	CodeCode Available	5
CAMAL: Optimizing LSM-trees via Active Learning	Sep 23, 2024	Active Learning	CodeCode Available	5
Bayesian Batch Active Learning as Sparse Subset Approximation	Aug 6, 2019	Active Learning	CodeCode Available	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