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

Showing 17111720 of 3073 papers

TitleStatusHype
Weight Decay Scheduling and Knowledge Distillation for Active Learning0
Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification0
Weighted Ensembles for Active Learning with Adaptivity0
Physics-informed active learning with simultaneous weak-form latent space dynamics identification0
What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper0
What can be learned from satisfaction assessments?0
What does the free energy principle tell us about the brain?0
What do I Annotate Next? An Empirical Study of Active Learning for Action Localization0
What is the Value of Data? On Mathematical Methods for Data Quality Estimation0
What I've learned about annotating informal text (and why you shouldn't take my word for it)0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified