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 1101–1125 of 3073 papers

TitleStatusHype
Active Learning for Online Recognition of Human Activities from Streaming Videos—0
Adaptive Region-Based Active Learning—0
Active Learning for One-Class Classification Using Two One-Class Classifiers—0
Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition—0
Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection—0
Adaptive quadrature schemes for Bayesian inference via active learning—0
Active Learning for Object Detection with Non-Redundant Informative Sampling—0
Active Label Refinement for Semantic Segmentation of Satellite Images—0
Adaptive network reliability analysis: Methodology and applications to power grid—0
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint—0
Active learning for object detection in high-resolution satellite images—0
Cost-effective Variational Active Entity Resolution—0
Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference—0
Importance sampling based active learning for parametric seismic fragility curve estimation—0
Active and sparse methods in smoothed model checking—0
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions—0
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification—0
Active Learning for Non-Parametric Choice Models—0
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost—0
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget—0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning—0
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks—0
Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints—0
Cost-Effective Training in Low-Resource Neural Machine Translation—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