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 591600 of 3073 papers

TitleStatusHype
CAMAL: Optimizing LSM-trees via Active LearningCode0
Building a comprehensive syntactic and semantic corpus of Chinese clinical textsCode0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processingCode0
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active LearningCode0
Class Balance Matters to Active Class-Incremental LearningCode0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
Black-Box Batch Active Learning for RegressionCode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
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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