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 1561–1570 of 3073 papers

TitleStatusHype
Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning—0
Analytic Mutual Information in Bayesian Neural Networks—0
Active Learning Polynomial Threshold Functions—0
HC4: A New Suite of Test Collections for Ad Hoc CLIRCode0
Partition-Based Active Learning for Graph Neural NetworksCode0
Batch versus Sequential Active Learning for Recommender Systems—0
Efficient Sampling-Based Bayesian Active Learning for synaptic characterization—0
Optimizing Active Learning for Low Annotation Budgets—0
Improving the quality control of seismic data through active learning—0
Improving Data Augmentation in Low-resource Question Answering with Active Learning in Multiple Stages—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