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 1231–1240 of 3073 papers

TitleStatusHype
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies—0
DECAL: DEployable Clinical Active Learning—0
Deciding when to stop: Efficient stopping of active learning guided drug-target prediction—0
Decision Trees for Function Evaluation - Simultaneous Optimization of Worst and Expected Cost—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning—0
Deep Active Learning for Anomaly Detection—0
Active Learning for Single Neuron Models with Lipschitz Non-Linearities—0
Deep Active Ensemble Sampling For Image Classification—0
Adaptive Active Learning for Image Classification—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