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 881–890 of 3073 papers

TitleStatusHype
Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning—0
Autonomous synthesis of metastable materials—0
AI For Fraud Awareness—0
AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery—0
AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems—0
AutoWS: Automated Weak Supervision Framework for Text Classification—0
Active Learning with Multiple Kernels—0
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model—0
Active Learning with Neural Networks: Insights from Nonparametric Statistics—0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping—0
Show:102550
← PrevPage 89 of 308Next →

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