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

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Showing 421–430 of 3073 papers

TitleStatusHype
Maximizing Information Gain in Privacy-Aware Active Learning of Email Anomalies—0
Active Learning with Simple Questions—0
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network modelsCode0
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)Code1
Active Preference Learning for Ordering Items In- and Out-of-sampleCode0
Learning Linear Utility Functions From Pairwise Comparison Queries—0
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
Uncertainty for Active Learning on Graphs—0
Generative Active Learning for the Search of Small-molecule Protein Binders—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