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 671–680 of 3073 papers

TitleStatusHype
A Machine-learning framework for automatic reference-free quality assessment in MRI—0
A Markovian Formalism for Active Querying—0
Algorithmic Connections Between Active Learning and Stochastic Convex Optimization—0
Active Learning for Wireless IoT Intrusion Detection—0
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping—0
Amortized Active Learning for Nonparametric Functions—0
Active Learning-Based Optimization of Scientific Experimental Design—0
Amortized nonmyopic active search via deep imitation learning—0
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion—0
ALEX: Active Learning based Enhancement of a Model's Explainability—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