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 20012010 of 3073 papers

TitleStatusHype
Exploring and Addressing Reward Confusion in Offline Preference Learning0
Exploring Connections Between Active Learning and Model Extraction0
Exploring Label Dependency in Active Learning for Phenotype Mapping0
Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers0
Exploring Representativeness and Informativeness for Active Learning0
Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning0
Exploring the Universe with SNAD: Anomaly Detection in Astronomy0
Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation0
Exponential Savings in Agnostic Active Learning through Abstention0
Exponentiated Gradient Exploration for Active Learning0
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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