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 29412950 of 3073 papers

TitleStatusHype
Optimizing annotation efforts to build reliable annotated corpora for training statistical models0
Active Learning in Noisy Conditions for Spoken Language Understanding0
Targeting Optimal Active Learning via Example Quality0
Anytime Active LearningCode0
Bayesian Nonparametric Crowdsourcing0
Learning Privately with Labeled and Unlabeled Examples0
Beyond Disagreement-based Agnostic Active Learning0
Active Learning and Best-Response Dynamics0
Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition0
Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno0
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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