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 1001–1010 of 3073 papers

TitleStatusHype
Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition—0
On Active Learning for Gaussian Process-based Global Sensitivity Analysis—0
Active learning for fast and slow modeling attacks on Arbiter PUFs—0
Efficient Epistemic Uncertainty Estimation in Regression Ensemble Models Using Pairwise-Distance Estimators—0
Deep Active Audio Feature Learning in Resource-Constrained EnvironmentsCode0
A Bayesian Active Learning Approach to Comparative Judgement—0
Human Comprehensible Active Learning of Genome-Scale Metabolic Networks—0
Test-time augmentation-based active learning and self-training for label-efficient segmentationCode0
Overcoming Overconfidence for Active LearningCode0
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active LearningCode0
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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