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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1221–1230 of 3073 papers

TitleStatusHype
Active Learning for Chinese Word Segmentation—0
A Simple Approximation Algorithm for Optimal Decision Tree—0
A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images—0
A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks—0
Active Learning on Medical Image—0
Active Learning for Breast Cancer Identification—0
Active Learning Guided by Efficient Surrogate Learners—0
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal—0
A Scalable Algorithm for Active Learning—0
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified