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 1091–1100 of 3073 papers

TitleStatusHype
AutoWS: Automated Weak Supervision Framework for Text Classification—0
AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems—0
AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery—0
Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review—0
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation—0
Autonomous synthesis of metastable materials—0
Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning—0
Active Learning with Logged Data—0
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning—0
Active Learning with Label Comparisons—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