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 1991–2000 of 3073 papers

TitleStatusHype
Experiments on Active Learning for Croatian Word Sense Disambiguation—0
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience—0
Explanation-Based Attention for Semi-Supervised Deep Active Learning—0
Exploiting Context for Robustness to Label Noise in Active Learning—0
Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models—0
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning—0
Exploiting Structure in Representation of Named Entities using Active Learning—0
Explore-Exploit: A Framework for Interactive and Online Learning—0
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling—0
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers—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