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 751–760 of 3073 papers

TitleStatusHype
Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample AssessmentCode0
On Active Learning for Gaussian Process-based Global Sensitivity Analysis—0
Active learning for fast and slow modeling attacks on Arbiter PUFs—0
Deep Active Audio Feature Learning in Resource-Constrained EnvironmentsCode0
A Bayesian Active Learning Approach to Comparative Judgement—0
Efficient Epistemic Uncertainty Estimation in Regression Ensemble Models Using Pairwise-Distance Estimators—0
Human Comprehensible Active Learning of Genome-Scale Metabolic Networks—0
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic SegmentationCode1
Overcoming Overconfidence for Active LearningCode0
Test-time augmentation-based active learning and self-training for label-efficient segmentationCode0
Show:102550
← PrevPage 76 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