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 1851–1860 of 3073 papers

TitleStatusHype
Active learning for medical code assignment—0
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes—0
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity—0
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations—0
Active learning using weakly supervised signals for quality inspection—0
Low-Regret Active learning—0
Automated Performance Testing Based on Active Deep LearningCode0
Fast Design Space Exploration of Nonlinear Systems: Part II—0
Stopping Criterion for Active Learning Based on Error StabilityCode0
Fast Design Space Exploration of Nonlinear Systems: Part I—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