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 1201–1210 of 3073 papers

TitleStatusHype
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression—0
Active Learning Principles for In-Context Learning with Large Language Models—0
Active Learning for Community Detection in Stochastic Block Models—0
Active Deep Learning on Entity Resolution by Risk Sampling—0
Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization—0
Active Learning Polynomial Threshold Functions—0
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction—0
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment—0
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation—0
A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature—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