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 2111–2120 of 3073 papers

TitleStatusHype
Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning—0
Gravix: Active Learning for Gravitational Waves Classification Algorithms—0
Greedy Active Learning Algorithm for Logistic Regression Models—0
Greedy SLIM: A SLIM-Based Approach For Preference Elicitation—0
Guess What's on my Screen? Clustering Smartphone Screenshots with Active Learning—0
Guideline Learning for In-context Information Extraction—0
Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification—0
HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling—0
Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning—0
Hallucination Diversity-Aware Active Learning for Text Summarization—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