SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1281–1290 of 3073 papers

TitleStatusHype
Confidence Estimation for Object Detection in Document Images—0
Confidence Decision Trees via Online and Active Learning for Streaming (BIG) Data—0
Deep Bayesian Active Learning, A Brief Survey on Recent Advances—0
Deep Bayesian Active Learning for Multiple Correct Outputs—0
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data—0
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity—0
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion—0
Adaptive Active Hypothesis Testing under Limited Information—0
Show:102550
← PrevPage 129 of 308Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified