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 2701–2725 of 3073 papers

TitleStatusHype
Active Semi-Supervised Learning Using Sampling Theory for Graph SignalsCode0
Active Selection of Classification FeaturesCode0
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?Code0
Disentanglement based Active LearningCode0
MyriadAL: Active Few Shot Learning for HistopathologyCode0
Dissimilar Nodes Improve Graph Active LearningCode0
DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software EcosystemCode0
Integrating Deep Metric Learning with Coreset for Active Learning in 3D SegmentationCode0
SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and DetectionCode0
An Adversarial Objective for Scalable ExplorationCode0
Preference-based Interactive Multi-Document SummarisationCode0
Distributional Gradient Matching for Learning Uncertain Neural Dynamics ModelsCode0
Active Preference Learning for Ordering Items In- and Out-of-sampleCode0
Stopping Criterion for Active Learning Based on Error StabilityCode0
Nearest Neighbor Classifier with Margin Penalty for Active LearningCode0
Interactively Teaching an Inverse Reinforcement Learner with Limited FeedbackCode0
The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive AnnotationCode0
Near-Optimal Active Learning of Multi-Output Gaussian ProcessesCode0
Distribution Discrepancy and Feature Heterogeneity for Active 3D Object DetectionCode0
Interactive Refinement of Cross-Lingual Word EmbeddingsCode0
Active Few-Shot Learning with FASLCode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
Diversity-Aware Batch Active Learning for Dependency ParsingCode0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
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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