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 401410 of 3073 papers

TitleStatusHype
Active Learning for Classifying 2D Grid-Based Level CompletabilityCode0
Cost-effective Object Detection: Active Sample Mining with Switchable Selection CriteriaCode0
Confidence Estimation Using Unlabeled DataCode0
Confidence-Aware Active Feedback for Interactive Instance SearchCode0
Active Learning of Spin Network ModelsCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
Compute-Efficient Active LearningCode0
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical GuaranteesCode0
A3: Active Adversarial Alignment for Source-Free Domain AdaptationCode0
Computational Assessment of Hyperpartisanship in News TitlesCode0
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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