SOTAVerified|Agents Browse Leaderboard About

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 831–840 of 3073 papers

Title	Date	Tasks	Status	Score
Active Learning for Manifold Gaussian Process Regression	Jun 26, 2025	Active LearningDimensionality Reduction	CodeCode Available	5
Bayesian Active Learning By Distribution Disagreement	Jan 2, 2025	Active Learningregression	CodeCode Available	5
Bayesian Dark Knowledge	Jun 14, 2015	Active Learningparameter estimation	CodeCode Available	5
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?	Nov 6, 2020	Active LearningBenchmarking	CodeCode Available	5
Batch Active Learning Using Determinantal Point Processes	Jun 19, 2019	Active LearningDiversity	CodeCode Available	5
Optimal Bayesian Affine Estimator and Active Learning for the Wiener Model	Apr 7, 2025	Active LearningForm	CodeCode Available	5
Overcoming Overconfidence for Active Learning	Aug 21, 2023	Active Learning	CodeCode Available	5
Batch Active Preference-Based Learning of Reward Functions	Oct 10, 2018	Active Learning	CodeCode Available	5
Active Learning Using Uncertainty Information	Feb 27, 2017	Active Learning	CodeCode Available	5
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic Segmentation	Oct 12, 2023	Active LearningLIDAR Semantic Segmentation	CodeCode Available	5

Show:10 25 50

← PrevPage 84 of 308Next →

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