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 27012710 of 3073 papers

TitleStatusHype
Active Semi-Supervised Learning Using Sampling Theory for Graph SignalsCode0
Active Selection of Classification FeaturesCode0
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?Code0
Disentanglement based Active LearningCode0
MyriadAL: Active Few Shot Learning for HistopathologyCode0
Dissimilar Nodes Improve Graph Active LearningCode0
DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software EcosystemCode0
Integrating Deep Metric Learning with Coreset for Active Learning in 3D SegmentationCode0
SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and DetectionCode0
An Adversarial Objective for Scalable ExplorationCode0
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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