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 2401–2410 of 3073 papers

TitleStatusHype
Meta-Active Learning for Node Response Prediction in Graphs—0
Meta-active Learning in Probabilistically-Safe Optimization—0
Meta Agent Teaming Active Learning for Pose Estimation—0
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps—0
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning—0
Method51 for Mining Insight from Social Media Datasets—0
MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling—0
mfEGRA: Multifidelity Efficient Global Reliability Analysis through Active Learning for Failure Boundary Location—0
MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation—0
Midas Loop: A Prioritized Human-in-the-Loop Annotation for Large Scale Multilayer Data—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