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 21012110 of 3073 papers

TitleStatusHype
Minimizing Supervision in Multi-label Categorization0
Discriminative Active Learning for Domain Adaptation0
Active Learning for Skewed Data Sets0
Batch Decorrelation for Active Metric LearningCode0
Stopping criterion for active learning based on deterministic generalization bounds0
VirAAL: Virtual Adversarial Active Learning For NLUCode0
Empowering Active Learning to Jointly Optimize System and User DemandsCode0
Active Learning with Multiple Kernels0
Deeply Supervised Active Learning for Finger Bones SegmentationCode0
Modeling nanoconfinement effects using 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