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 2311–2320 of 3073 papers

TitleStatusHype
Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction—0
Active learning for binary classification with variable selection—0
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces—0
Active Learning for Breast Cancer Identification—0
Active Learning for Chinese Word Segmentation—0
NE-LP: Normalized Entropy and Loss Prediction based Sampling for Active Learning in Chinese Word Segmentation on EHRs—0
Active Learning for Community Detection in Stochastic Block Models—0
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models—0
Active Learning for Contextual Search with Binary Feedbacks—0
Active Learning for Continual Learning: Keeping the Past Alive in the Present—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