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 911–920 of 3073 papers

TitleStatusHype
Batch Multi-Fidelity Active Learning with Budget Constraints—0
Batch versus Sequential Active Learning for Recommender Systems—0
BayesFormer: Transformer with Uncertainty Estimation—0
Bayesian Active Edge Evaluation on Expensive Graphs—0
Bayesian Active Learning by Disagreements: A Geometric Perspective—0
Active Learning with TensorBoard Projector—0
Bayesian Active Learning for Censored Regression—0
Bayesian active learning for choice models with deep Gaussian processes—0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—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