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 27412750 of 3073 papers

TitleStatusHype
Anytime Active LearningCode0
DQI: Measuring Data Quality in NLPCode0
Uncertainty Estimation of Transformer Predictions for Misclassification DetectionCode0
Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier DetectionCode0
DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image SegmentationCode0
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and SolutionsCode0
Dual Active Sampling on Batch-Incremental Active LearningCode0
Neural Active Learning on Heteroskedastic DistributionsCode0
ScatterShot: Interactive In-context Example Curation for Text TransformationCode0
DUAL: Diversity and Uncertainty Active Learning for Text SummarizationCode0
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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