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 851–860 of 3073 papers

TitleStatusHype
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
Active Learning for Saddle Point Calculation—0
A unified framework for learning with nonlinear model classes from arbitrary linear samples—0
Aurora: Are Android Malware Classifiers Reliable and Stable under Distribution Shift?—0
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth—0
A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences—0
Active Learning with Constrained Topic Model—0
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning—0
Auto-Differentiating Linear Algebra—0
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth—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