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 2341–2350 of 3073 papers

TitleStatusHype
Curriculum learning for data-driven modeling of dynamical systems—0
Leveraging Variation Theory in Counterfactual Data Augmentation for Optimized Active Learning—0
LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation—0
Limitations of Active Learning With Deep Transformer Language Models—0
Limitations of Assessing Active Learning Performance at Runtime—0
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design—0
Lipschitz Adaptivity with Multiple Learning Rates in Online Learning—0
Lirot.ai: A Novel Platform for Crowd-Sourcing Retinal Image Segmentations—0
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection—0
LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point CLoud Active Learning—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