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 381–390 of 3073 papers

TitleStatusHype
Active Learning for Deep Object Detection—0
Active Learning for Deep Neural Networks on Edge Devices—0
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation—0
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation—0
Active Domain Adaptation with False Negative Prediction for Object Detection—0
A critical look at the current train/test split in machine learning—0
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
Active Learning with Logged Data—0
A Contextual Bandit Approach for Stream-Based Active Learning—0
Active Learning with Expert Advice—0
Show:102550
← PrevPage 39 of 308Next →

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