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 16011610 of 3073 papers

TitleStatusHype
Active Learning for Interactive Relation Extraction in a French Newspaper’s Articles0
Active Learning for Assisted Corpus Construction: A Case Study in Knowledge Discovery from Biomedical Text0
BERT-PersNER: A New Model for Persian Named Entity Recognition0
Headnote Prediction Using Machine Learning0
TAR on Social Media: A Framework for Online Content ModerationCode0
Certifying One-Phase Technology-Assisted Reviews0
Reducing Label Effort: Self-Supervised meets Active Learning0
Fluent: An AI Augmented Writing Tool for People who StutterCode1
Influence Selection for Active LearningCode1
Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays0
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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