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 28612870 of 3073 papers

TitleStatusHype
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning0
Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty0
Federated Learning with Uncertainty via Distilled Predictive Distributions0
Bayesian Generative Active Deep Learning0
Bayesian Hypernetworks0
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method0
Bayesian Nonparametric Crowdsourcing0
Bayesian optimization for robust robotic grasping using a sensorized compliant hand0
Bayesian Pool-based Active Learning With Abstention Feedbacks0
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure0
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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