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 1321–1330 of 3073 papers

TitleStatusHype
Stealing and Evading Malware Classifiers and Antivirus at Low False Positive ConditionsCode0
Benchmarking Active Learning Strategies for Materials Optimization and Discovery—0
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification—0
Active Learning with Label Comparisons—0
Active-learning-based non-intrusive Model Order Reduction—0
CrudeOilNews: An Annotated Crude Oil News Corpus for Event ExtractionCode0
Task-Aware Active Learning for Endoscopic Image AnalysisCode0
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations—0
Parameter Filter-based Event-triggered Learning—0
An Exploration of Active Learning for Affective Digital Phenotyping—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