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 401–410 of 3073 papers

TitleStatusHype
Make Safe Decisions in Power System: Safe Reinforcement Learning Based Pre-decision Making for Voltage Stability Emergency Control—0
Active Learning for Finely-Categorized Image-Text Retrieval by Selecting Hard Negative Unpaired Samples—0
Amortized nonmyopic active search via deep imitation learning—0
Lower Bound on the Greedy Approximation Ratio for Adaptive Submodular Cover—0
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models—0
Actively Learning Combinatorial Optimization Using a Membership Oracle—0
Smooth Pseudo-Labeling—0
Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation—0
What Makes Good Few-shot Examples for Vision-Language Models?—0
An Active Learning Framework with a Class Balancing Strategy for Time Series 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