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 25312540 of 3073 papers

TitleStatusHype
Deep Bayesian Active Learning for Accelerating Stochastic SimulationCode0
MCAL: Minimum Cost Human-Machine Active LabelingCode0
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly DetectionCode0
Mining GOLD Samples for Conditional GANsCode0
CrudeOilNews: An Annotated Crude Oil News Corpus for Event ExtractionCode0
Wide Contextual Residual Network with Active Learning for Remote Sensing Image ClassificationCode0
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition CharacteristicsCode0
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian ProcessesCode0
Curiosity Driven Exploration to Optimize Structure-Property Learning in MicroscopyCode0
Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security ApplicationsCode0
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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