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 551–560 of 3073 papers

TitleStatusHype
Active Learning of Mealy Machines with Timers—0
Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty—0
Adversarial Sampling for Active Learning—0
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection—0
Active Learning of Linear Embeddings for Gaussian Processes—0
Active learning for affinity prediction of antibodies—0
Active Learning for Accurate Estimation of Linear Models—0
Active Learning of General Halfspaces: Label Queries vs Membership Queries—0
Active Data Discovery: Mining Unknown Data using Submodular Information Measures—0
Minimum-Margin Active Learning—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