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 371–380 of 3073 papers

TitleStatusHype
Active Learning with Expert Advice—0
Active Learning with Label Comparisons—0
Active Learning with Neural Networks: Insights from Nonparametric Statistics—0
Active Learning for Delineation of Curvilinear Structures—0
Active Learning for Deep Visual Tracking—0
ActiveDP: Bridging Active Learning and Data Programming—0
Active learning for deep semantic parsing—0
Batch Active Learning in Gaussian Process Regression using Derivatives—0
Active learning with biased non-response to label requests—0
Active Learning for Deep Object Detection—0
Show:102550
← PrevPage 38 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