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 2181–2190 of 3073 papers

TitleStatusHype
Learning Non-Markovian Reward Models in MDPs—0
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience—0
Active Learning for Entity AlignmentCode0
Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics—0
Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning—0
Active and Incremental Learning with Weak Supervision—0
Projection based Active Gaussian Process Regression for Pareto Front Modeling—0
Active Learning over DNN: Automated Engineering Design Optimization for Fluid Dynamics Based on Self-Simulated Dataset—0
K-NN active learning under local smoothness assumption—0
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition—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