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 11311140 of 3073 papers

TitleStatusHype
Context-aware Active Multi-Step Reinforcement Learning0
Context Aware Image Annotation in Active Learning0
Context-Aware Query Selection for Active Learning in Event Recognition0
Context-driven Active and Incremental Activity Recognition0
CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance0
Contextual Bayesian optimization with binary outputs0
CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data0
Multi-View Active Learning for Short Text Classification in User-Generated Data0
Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition0
Binary Classification with XOR Queries: Fundamental Limits and An Efficient Algorithm0
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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