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 2361–2370 of 3073 papers

TitleStatusHype
Lower Bounds for Passive and Active Learning—0
Lower Bounds on Active Learning for Graphical Model Selection—0
Low-Regret Active learning—0
Low-Resolution Face Recognition In Resource-Constrained Environments—0
Low-resource Deep Entity Resolution with Transfer and Active Learning—0
LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environment—0
Machine Learning Algorithms for Data Labeling: An Empirical Evaluation—0
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition—0
Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization—0
Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system—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