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 2221–2230 of 3073 papers

TitleStatusHype
Active and sparse methods in smoothed model checking—0
ActiveAnno: General-Purpose Document-Level Annotation Tool with Active Learning Integration—0
Active anomaly detection based on deep one-class classification—0
Active Anomaly Detection for time-domain discoveries—0
Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers—0
Bucketized Active Sampling for Learning ACOPF—0
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions—0
Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner—0
Active classification with comparison queries—0
ActiveClean: Generating Line-Level Vulnerability Data via Active 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