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 1631–1640 of 3073 papers

TitleStatusHype
On Statistical Bias In Active Learning: How and When To Fix It—0
On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search—0
On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning—0
Active learning of effective Hamiltonian for super-large-scale atomic structures—0
On the Geometry of Deep Bayesian Active Learning—0
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks—0
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning—0
On the Limitations of Simulating Active Learning—0
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?—0
On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise—0
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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