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 1831–1840 of 3073 papers

TitleStatusHype
An efficient scheme based on graph centrality to select nodes for training for effective learning—0
Diversity-Aware Batch Active Learning for Dependency ParsingCode0
Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning—0
Morphological classification of astronomical images with limited labelling—0
Multi-class Text Classification using BERT-based Active Learning—0
Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier DetectionCode0
Active Learning of Sequential Transducers with Side Information about the Domain—0
One-Round Active Learning—0
AdaptiFont: Increasing Individuals' Reading Speed with a Generative Font Model and Bayesian Optimization—0
Active and sparse methods in smoothed model checking—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