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 1721–1730 of 3073 papers

TitleStatusHype
ImitAL: Learning Active Learning Strategies from Synthetic DataCode0
A Comparison of Strategies for Source-Free Domain Adaptation—0
Neural Predictive Monitoring under Partial ObservabilityCode0
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc ExplainabilityCode0
Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages—0
Towards Visual Explainable Active Learning for Zero-Shot Classification—0
Deep Active Learning for Text Classification with Diverse Interpretations—0
Jasmine: A New Active Learning Approach to Combat Cybercrime—0
Reinforcement Learning Approach to Active Learning for Image Classification—0
Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems—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