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 76–100 of 3073 papers

TitleStatusHype
Active Statistical InferenceCode1
Active Testing: Sample-Efficient Model EvaluationCode1
Active Bayesian Causal InferenceCode1
Active Learning for Coreference Resolution using Discrete AnnotationCode1
Active learning based generative design for the discovery of wide bandgap materialsCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Active Learning by Feature MixingCode1
Active Learning for Bayesian 3D Hand Pose EstimationCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
Active Learning for BERT: An Empirical StudyCode1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experimentsCode1
A Comparative Survey of Deep Active LearningCode1
Active Learning for Deep Object Detection via Probabilistic ModelingCode1
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain KnowledgeCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
AISecKG: Knowledge Graph Dataset for Cybersecurity EducationCode1
Active Sensing for Communications by LearningCode1
Active learning for medical image segmentation with stochastic batchesCode1
Active Learning for Open-set AnnotationCode1
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced DatasetsCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
Show:102550
← PrevPage 4 of 123Next →

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