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 401–410 of 3073 papers

TitleStatusHype
Actively Learning Combinatorial Optimization Using a Membership Oracle—0
Active Learning for Control-Oriented Identification of Nonlinear Systems—0
Active Learning for Continual Learning: Keeping the Past Alive in the Present—0
Active Dictionary Learning in Sparse Representation Based Classification—0
Active Learning for Contextual Search with Binary Feedbacks—0
Active Learning Solution on Distributed Edge Computing—0
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models—0
Active Dialogue Simulation in Conversational Systems—0
Active Learning: Sampling in the Least Probable Disagreement Region—0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—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