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 441450 of 3073 papers

TitleStatusHype
Distributional Gradient Matching for Learning Uncertain Neural Dynamics ModelsCode0
Active DOP: A constituency treebank annotation tool with online learningCode0
Exploring Data Redundancy in Real-world Image Classification through Data SelectionCode0
Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular SimulationCode0
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular RepresentationsCode0
Active Learning with Partial FeedbackCode0
Compute-Efficient Active LearningCode0
Computational Assessment of Hyperpartisanship in News TitlesCode0
Active Learning for Argument Strength EstimationCode0
Computational Job Market Analysis with Natural Language ProcessingCode0
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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