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 17111720 of 3073 papers

TitleStatusHype
Active learning for structural reliability: survey, general framework and benchmark0
Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains0
Tuning Deep Active Learning for Semantic Role Labeling0
Active learning and negative evidence for language identification0
Entity Prediction in Knowledge Graphs with Joint Embeddings0
Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation ModelsCode0
LATEX-Numeric: Language Agnostic Text Attribute Extraction for Numeric Attributes0
ActiveAnno: General-Purpose Document-Level Annotation Tool with Active Learning Integration0
FITAnnotator: A Flexible and Intelligent Text Annotation System0
OASIS: An Active Framework for Set Inversion0
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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