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 1331–1340 of 3073 papers

TitleStatusHype
A New Perspective on Pool-Based Active Classification and False-Discovery Control—0
Active learning of causal probability trees—0
Active Learning Enables Extrapolation in Molecular Generative Models—0
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions—0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets—0
Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy—0
An Empirical Study on the Efficacy of Deep Active Learning for Image Classification—0
An Efficient Active Learning Pipeline for Legal Text Classification—0
Active Learning of Abstract Plan Feasibility—0
Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation—0
Show:102550
← PrevPage 134 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