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 701–710 of 3073 papers

TitleStatusHype
An Adaptive Supervision Framework for Active Learning in Object Detection—0
Active Learning of Driving Scenario Trajectories—0
A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteria—0
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning—0
Analysis of Stopping Active Learning based on Stabilizing Predictions—0
Analytic Mutual Information in Bayesian Neural Networks—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
Analyzing Well-Formedness of Syllables in Japanese Sign Language—0
An Analysis and Visualization Tool for Case Study Learning of Linguistic Concepts—0
ALEVS: Active Learning by Statistical Leverage Sampling—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