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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 71–80 of 3073 papers

TitleStatusHype
Active Learning from the WebCode1
Active Bayesian Causal InferenceCode1
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
Active learning for medical image segmentation with stochastic batchesCode1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
Active Anomaly Detection via EnsemblesCode1
Active Learning for Open-set AnnotationCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
Active Learning for Deep Object Detection via Probabilistic ModelingCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
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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