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 27912800 of 3073 papers

TitleStatusHype
Image and Video Mining through Online Learning0
Towards Better Response Times and Higher-Quality Queries in Interactive Knowledge Base Debugging0
Classifying and sorting cluttered piles of unknown objects with robots: a learning approach0
Graph-Based Active Learning: A New Look at Expected Error Minimization0
Active Robust Learning0
Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty0
Can Active Learning Experience Be Transferred?0
Unsupervised Document Classification with Informed Topic Models0
Understanding Discourse on Work and Job-Related Well-Being in Public Social Media0
Active Learning for Dependency Parsing with Partial Annotation0
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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