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

Showing 10211030 of 3073 papers

TitleStatusHype
Can Active Learning Experience Be Transferred?0
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning0
Active Learning for Efficient Testing of Student Programs0
Towards Bayesian Data Selection0
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies0
Actively Learning Combinatorial Optimization Using a Membership Oracle0
Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data0
Bayesian Bias Mitigation for Crowdsourcing0
Actively learning a Bayesian matrix fusion model with deep side information0
Bayesian Active Summarization0
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Benchmark Results

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