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 10011010 of 3073 papers

TitleStatusHype
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance AssessmentCode1
HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling0
Semi-Automated Construction of Food Composition Knowledge BaseCode0
Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop0
Exploring Active 3D Object Detection from a Generalization PerspectiveCode1
Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning0
Active Learning of Piecewise Gaussian Process Surrogates0
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement PrioritizationCode0
Active learning for medical image segmentation with stochastic batchesCode1
Computational Assessment of Hyperpartisanship in News TitlesCode0
Show:102550
← PrevPage 101 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