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 19711980 of 3073 papers

TitleStatusHype
Subsequence Based Deep Active Learning for Named Entity Recognition0
Subspace Clustering with Active Learning0
Subspace-Distance-Enabled Active Learning for Efficient Data-Driven Model Reduction of Parametric Dynamical Systems0
Sufficient Conditions for Agnostic Active Learnable0
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation0
SUPClust: Active Learning at the Boundaries0
Superposition through Active Learning lens0
Supervised Negative Binomial Classifier for Probabilistic Record Linkage0
Supervising Feature Influence0
Support Vector Machine Active Learning Algorithms with Query-by-Committee versus Closest-to-Hyperplane Selection0
Show:102550
← PrevPage 198 of 308Next →

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