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 661–670 of 3073 papers

TitleStatusHype
Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems—0
An Artificial Intelligence (AI) workflow for catalyst design and optimization—0
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper—0
ALPINE: Active Link Prediction using Network Embedding—0
AL-PINN: Active Learning-Driven Physics-Informed Neural Networks for Efficient Sample Selection in Solving Partial Differential Equations—0
Active Learning Graph Neural Networks via Node Feature Propagation—0
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data—0
ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling—0
ALVIN: Active Learning Via INterpolation—0
Algorithmic Connections Between Active Learning and Stochastic Convex Optimization—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