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 841–850 of 3073 papers

TitleStatusHype
Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance—0
ALANNO: An Active Learning Annotation System for Mortals—0
A Lagrangian Duality Approach to Active Learning—0
Audio-Enhanced Vision-Language Modeling with Latent Space Broadening for High Quality Data Expansion—0
Auditing: Active Learning with Outcome-Dependent Query Costs—0
Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling—0
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Active Learning for Undirected Graphical Model Selection—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