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 341–350 of 3073 papers

TitleStatusHype
Active Learning for Event Extraction with Memory-based Loss Prediction Model—0
Active Learning for Event Detection in Support of Disaster Analysis Applications—0
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving—0
Active Learning for Binary Classification with Abstention—0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Active Few-Shot Fine-Tuning—0
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches—0
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings—0
Active learning for energy-based antibody optimization and enhanced screening—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