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 931–940 of 3073 papers

TitleStatusHype
Active Learning Approach to Optimization of Experimental Control—0
Bayesian Active Summarization—0
Actively learning a Bayesian matrix fusion model with deep side information—0
Bayesian Bias Mitigation for Crowdsourcing—0
Actively Learning Combinatorial Optimization Using a Membership Oracle—0
Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data—0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning—0
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies—0
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning—0
A Graph-Based Approach for Active Learning in Regression—0
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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