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 1321–1330 of 3073 papers

TitleStatusHype
Active Learning of Continuous-time Bayesian Networks through Interventions—0
An Eye-tracking Study of Named Entity Annotation—0
An Exploration of Active Learning for Affective Digital Phenotyping—0
Active Learning of Classifiers with Label and Seed Queries—0
Active Learning-Enhanced Dual Control for Angle-Only Initial Relative Orbit Determination—0
Active Curriculum Learning—0
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models—0
An Experimental Comparison of Active Learning Strategies for Partially Labeled Sequences—0
Active Learning of Causal Structures with Deep Reinforcement Learning—0
A New Vision of Collaborative Active Learning—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