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 22012210 of 3073 papers

TitleStatusHype
Active Learning in Video Tracking0
Active Learning for Segmentation Based on Bayesian Sample Queries0
Adversarial Representation Active LearningCode0
MedCAT -- Medical Concept Annotation Tool0
TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis0
When Your Robot Breaks: Active Learning During Plant Failure0
Incorporating Unlabeled Data into Distributionally Robust Learning0
Disentanglement based Active LearningCode0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing0
Parting with Illusions about Deep 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