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 24212430 of 3073 papers

TitleStatusHype
Mitigating sampling bias in risk-based active learning via an EM algorithm0
MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search0
Mode Estimation with Partial Feedback0
Model-based active learning to detect isometric deformable objects in the wild with deep architectures0
Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks0
Model Exploration with Cost-Aware Learning0
Modeling Human Annotation Errors to Design Bias-Aware Systems for Social Stream Processing0
Modeling nanoconfinement effects using active learning0
Modelling Human Active Search in Optimizing Black-box Functions0
Model Rectification via Unknown Unknowns Extraction from Deployment Samples0
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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