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

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Showing 511–520 of 3073 papers

TitleStatusHype
Active Learning on Synthons for Molecular Design—0
Active Learning for Chinese Word Segmentation—0
Active Learning for Breast Cancer Identification—0
Active Learning on Medical Image—0
Active Learning Guided by Efficient Surrogate Learners—0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling—0
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation—0
Active Learning on a Programmable Photonic Quantum Processor—0
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces—0
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation—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