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 1291–1300 of 3073 papers

TitleStatusHype
A Novel Ensemble Learning Approach to Unsupervised Record Linkage—0
A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteria—0
A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis—0
An optimal transport approach for selecting a representative subsample with application in efficient kernel density estimation—0
Active Learning of Linear Embeddings for Gaussian Processes—0
Active learning for affinity prediction of antibodies—0
Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning—0
Anomaly Detection in Hierarchical Data Streams under Unknown Models—0
Active Learning of General Halfspaces: Label Queries vs Membership Queries—0
Active Learning for Accurate Estimation of Linear Models—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