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 1761–1770 of 3073 papers

TitleStatusHype
Adapting Coreference Resolution Models through Active LearningCode0
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems—0
Comparative Study of Learning Outcomes for Online Learning Platforms—0
Understanding the Eluder Dimension—0
Can Active Learning Preemptively Mitigate Fairness Issues?Code1
Model Learning with Personalized Interpretability Estimation (ML-PIE)Code1
All you need are a few pixels: semantic segmentation with PixelPickCode1
Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation—0
Active learning for medical code assignment—0
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity—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