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 2521–2530 of 3073 papers

TitleStatusHype
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios—0
Actively learning a Bayesian matrix fusion model with deep side information—0
Actively Learning Combinatorial Optimization Using a Membership Oracle—0
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies—0
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning—0
Actively Learning Hemimetrics with Applications to Eliciting User Preferences—0
Actively learning to learn causal relationships—0
Actively Learning what makes a Discrete Sequence Valid—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
Active metric learning and classification using similarity queries—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