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 1451–1460 of 3073 papers

TitleStatusHype
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations—0
Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems—0
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models—0
Efficient Label Collection for Unlabeled Image Datasets—0
Efficient Learning of Linear Separators under Bounded Noise—0
An Exploration of Active Learning for Affective Digital Phenotyping—0
Efficiently labelling sequences using semi-supervised active learning—0
Active and passive learning of linear separators under log-concave distributions—0
Efficient Named Entity Annotation through Pre-empting—0
Comprehensive Benchmarking of Entropy and Margin Based Scoring Metrics for Data Selection—0
Show:102550
← PrevPage 146 of 308Next →

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