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 2781–2790 of 3073 papers

TitleStatusHype
A Survey of Latent Factor Models in Recommender Systems—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis—0
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification—0
A Survey on Deep Active Learning: Recent Advances and New Frontiers—0
Active learning for data streams: a survey—0
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams—0
A Survey on Uncertainty Quantification Methods for Deep Learning—0
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables—0
Asymptotic Analysis of Objectives based on Fisher Information in 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