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 30413050 of 3073 papers

TitleStatusHype
"Give Me an Example Like This": Episodic Active Reinforcement Learning from DemonstrationsCode0
A Survey on Multi-Task LearningCode0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
An active learning method for solving competitive multi-agent decision-making and control problemsCode0
Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample AssessmentCode0
A Survey on Deep Learning of Small Sample in Biomedical Image AnalysisCode0
Rethinking deep active learning: Using unlabeled data at model trainingCode0
Gradient and Uncertainty Enhanced Sequential Sampling for Global FitCode0
Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based ApproachCode0
Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence MaximizationCode0
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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