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 25912600 of 3073 papers

TitleStatusHype
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint0
Adaptive network reliability analysis: Methodology and applications to power grid0
Adaptive quadrature schemes for Bayesian inference via active learning0
Adaptive Region-Based Active Learning0
Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties0
Adaptive Selective Sampling for Online Prediction with Experts0
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization0
Adaptive Submodular Ranking and Routing0
Adaptivity in Adaptive Submodularity0
Adaptivity to Noise Parameters in Nonparametric Active Learning0
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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