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 1071–1080 of 3073 papers

TitleStatusHype
Active Learning for Regression by Inverse Distance Weighting—0
A domain-decomposed VAE method for Bayesian inverse problems—0
AdjointNet: Constraining machine learning models with physics-based codes—0
Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal—0
Active anomaly detection based on deep one-class classification—0
A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections—0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
Active Learning for Product Type Ontology Enhancement in E-commerce—0
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion—0
Active Learning and Novel Model Calibration Measurements for Automated Visual Inspection in Manufacturing—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