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

Showing 27112720 of 3073 papers

TitleStatusHype
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified