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 1401–1410 of 3073 papers

TitleStatusHype
An Adaptive Strategy for Active Learning with Smooth Decision Boundary—0
Downstream-Pretext Domain Knowledge Traceback for Active Learning—0
Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators' Concerns and Behaviors—0
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy—0
Active Learning of Driving Scenario Trajectories—0
DroidStar: Callback Typestates for Android Classes—0
Efficient Active Learning for Gaussian Process Classification by Error Reduction—0
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation—0
Dual Active Learning for Reinforcement Learning from Human Feedback—0
Comprehensively identifying Long Covid articles with human-in-the-loop machine 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