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 1591–1600 of 3073 papers

TitleStatusHype
Nuances in Margin Conditions Determine Gains in Active Learning—0
Nuclear Discrepancy for Active Learning—0
O2U-Net: A Simple Noisy Label Detection Approach for Deep Neural Networks—0
OASIS: An Active Framework for Set Inversion—0
Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning—0
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression—0
Omnibus Dropout for Improving The Probabilistic Classification Outputs of ConvNets—0
Omni-Mol: Exploring Universal Convergent Space for Omni-Molecular Tasks—0
On Active Learning for Gaussian Process-based Global Sensitivity Analysis—0
On Computability, Learnability and Extractability of Finite State Machines from Recurrent Neural Networks—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