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 201210 of 3073 papers

TitleStatusHype
Model-Change Active Learning in Graph-Based Semi-Supervised LearningCode1
Class-Balanced Active Learning for Image ClassificationCode1
Hitting the Target: Stopping Active Learning at the Cost-Based OptimumCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
Unsupervised Selective Labeling for More Effective Semi-Supervised LearningCode1
AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active LearningCode1
Cartography Active LearningCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Fluent: An AI Augmented Writing Tool for People who StutterCode1
Influence Selection for Active LearningCode1
Show:102550
← PrevPage 21 of 308Next →

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