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 1461–1470 of 3073 papers

TitleStatusHype
Distance-Penalized Active Learning Using Quantile Search—0
An Active Learning Based Approach For Effective Video Annotation And Retrieval—0
Active Learning in Gaussian Process State Space Model—0
DISPATCH: Design Space Exploration of Cyber-Physical Systems—0
An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data—0
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation—0
Discwise Active Learning for LiDAR Semantic Segmentation—0
Discriminative Batch Mode Active Learning—0
ELAD: Explanation-Guided Large Language Models Active Distillation—0
Discriminative Active Learning for Domain Adaptation—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