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 1731–1740 of 3073 papers

TitleStatusHype
Active Learning for Saddle Point Calculation—0
Probabilistic Active Learning for Active Class Selection—0
Reducing Annotating Load: Active Learning with Synthetic Images in Surgical Instrument SegmentationCode0
Active Learning of Driving Scenario Trajectories—0
Self-supervised optimization of random material microstructures in the small-data regimeCode0
Investigating Active Learning in Interactive Neural Machine Translation—0
The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation—0
Active Curriculum Learning—0
Subsequence Based Deep Active Learning for Named Entity Recognition—0
Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence MaximizationCode0
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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