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 831840 of 3073 papers

TitleStatusHype
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning0
A Survey on Uncertainty Quantification Methods for Deep Learning0
Asymptotic Accuracy of Distribution-Based Estimation for Latent Variables0
Asymptotic Analysis of Objectives based on Fisher Information in Active Learning0
Information Losses in Neural Classifiers from Sampling0
A System for Generating Multiple Choice Questions: With a Novel Approach for Sentence Selection0
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree0
ALARM: Active LeArning of Rowhammer Mitigations0
Active Learning for Video Classification with Frame Level Queries0
ALANNO: An Active Learning Annotation System for Mortals0
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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