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 2301–2310 of 3073 papers

TitleStatusHype
Learning in Unlabeled Networks - An Active Learning and Inference Approach—0
Learning Linear Utility Functions From Pairwise Comparison Queries—0
Learning Nash Equilibrial Hamiltonian for Two-Player Collision-Avoiding Interactions—0
Learning Non-Markovian Reward Models in MDPs—0
Learning Optimal Antenna Tilt Control Policies: A Contextual Linear Bandit Approach—0
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints—0
Learning Privately with Labeled and Unlabeled Examples—0
Learning Quantitative Automata Modulo Theories—0
Learning Rare Category Classifiers on a Tight Labeling Budget—0
Learning Salient Samples and Distributed Representations for Topic-Based Chinese Message Polarity Classification—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