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

Showing 19912000 of 3073 papers

TitleStatusHype
Targeted Active Learning for Bayesian Decision-Making0
Target-Independent Active Learning via Distribution-Splitting0
Targeting Optimal Active Learning via Example Quality0
Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders0
Task-Aware Variational Adversarial Active Learning0
Task Selection for Bandit-Based Task Assignment in Heterogeneous Crowdsourcing0
Teacher's Perception in the Classroom0
Teaching an Active Learner with Contrastive Examples0
Teaching Digital Signal Processing by Partial Flipping, Active Learning and Visualization0
Teaching Interactively to Learn Emotions in Natural Language0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified