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 14111420 of 3073 papers

TitleStatusHype
Optimal Data Selection: An Online Distributed ViewCode0
How Low Can We Go? Pixel Annotation for Semantic Segmentation0
DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt Identification using Semi-Supervised Learning0
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection0
ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI0
Cold Start Active Learning Strategies in the Context of Imbalanced Classification0
Active Learning Polynomial Threshold Functions0
Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning0
Analytic Mutual Information in Bayesian Neural Networks0
HC4: A New Suite of Test Collections for Ad Hoc CLIRCode0
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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