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 1681–1690 of 3073 papers

TitleStatusHype
Unsupervised Clustering Active Learning for Person Re-identification—0
Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion—0
Unsupervised Document Classification with Informed Topic Models—0
Unsupervised Frequent Pattern Mining for CEP—0
Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection—0
Unsupervised Person Slot Filling based on Graph Mining—0
UPM system for WMT 2012—0
Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits—0
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—0
Using Active Learning Methods to Strategically Select Essays for Automated Scoring—0
Show:102550
← PrevPage 169 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