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 16811690 of 3073 papers

TitleStatusHype
Phrase-level Active Learning for Neural Machine Translation0
ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument AggregationCode0
Active and Dynamic Beam Tracking UnderStochastic Mobility0
Active Learning for Deep Neural Networks on Edge Devices0
Corruption Robust Active Learning0
Quality-Aware Memory Network for Interactive Volumetric Image SegmentationCode1
Transferable Query Selection for Active Domain Adaptation0
Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs0
Heuristic Stopping Rules For Technology-Assisted Review0
On Minimizing Cost in Legal Document Review WorkflowsCode1
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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