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 941–950 of 3073 papers

TitleStatusHype
Active Multi-Information Source Bayesian Quadrature—0
Active Learning for Fair and Stable Online Allocations—0
Active Model Aggregation via Stochastic Mirror Descent—0
Active Learning for Event Extraction with Memory-based Loss Prediction Model—0
Active Mining Sample Pair Semantics for Image-text Matching—0
Active Metric Learning from Relative Comparisons—0
Active Learning for Event Detection in Support of Disaster Analysis Applications—0
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment—0
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving—0
LLMs as Probabilistic Minimally Adequate Teachers for DFA 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