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 27512760 of 3073 papers

TitleStatusHype
Diameter-Based Active Learning0
Active Learning Using Uncertainty InformationCode0
Generative Adversarial Active Learning0
Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning0
Learning to Multi-Task by Active SamplingCode0
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces0
Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization0
Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning0
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper0
DroidStar: Callback Typestates for Android Classes0
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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