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 2981–2990 of 3073 papers

TitleStatusHype
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion—0
Buy-in-Bulk Active Learning—0
Σ-Optimality for Active Learning on Gaussian Random Fields—0
Recommending with an Agenda: Active Learning of Private Attributes using Matrix Factorization—0
Beating the Minimax Rate of Active Learning with Prior Knowledge—0
Active Learning for Dependency Parsing by A Committee of Parsers—0
Para-active learning—0
Active Learning of Linear Embeddings for Gaussian Processes—0
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods—0
An Efficient Active Learning Framework for New Relation Types—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