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 14711480 of 3073 papers

TitleStatusHype
Predicting Difficulty and Discrimination of Natural Language Questions0
Uncertainty Estimation of Transformer Predictions for Misclassification DetectionCode0
From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)0
Data Uncertainty without Prediction Models0
Label a Herd in Minutes: Individual Holstein-Friesian Cattle IdentificationCode0
Towards Fewer Labels: Support Pair Active Learning for Person Re-identification0
Active Few-Shot Learning with FASLCode0
Active Learning with Weak Supervision for Gaussian ProcessesCode0
Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint0
Stream-based Active Learning with Verification Latency in Non-stationary EnvironmentsCode0
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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