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 2551–2575 of 3073 papers

TitleStatusHype
Active Regression by Stratification—0
Active Regression via Linear-Sample Sparsification—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings—0
Active Reinforcement Learning Strategies for Offline Policy Improvement—0
Active Relation Discovery: Towards General and Label-aware OpenRE—0
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction—0
Active Robust Learning—0
Active Sampling of Pairs and Points for Large-scale Linear Bipartite Ranking—0
Active Scene Learning—0
Active search for Bifurcations—0
Active Self-Paced Learning for Cost-Effective and Progressive Face Identification—0
Active Self-Semi-Supervised Learning for Few Labeled Samples—0
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need—0
Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation—0
Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistency—0
Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration—0
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space—0
Active Sentiment Domain Adaptation—0
ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for Long-Tailed Megakaryocyte Classification—0
Active Stacking for Heart Rate Estimation—0
Active Test-time Vision-Language Navigation—0
Active Third-Person Imitation Learning—0
Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations—0
Active Transfer Learning for Persian Offline Signature Verification—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