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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 921–930 of 3073 papers

TitleStatusHype
Bayesian Active Learning for Multi-Criteria Comparative Judgement in Educational Assessment—0
Active learning with version spaces for object detection—0
Active Learning for Structured Prediction from Partially Labeled Data—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Bayesian Active Learning for Structured Output Design—0
Bayesian Active Learning for Wearable Stress and Affect Detection—0
Bayesian Active Learning of (small) Quantile Sets through Expected Estimator Modification—0
Bayesian Active Learning With Abstention Feedbacks—0
Active Learning Approach to Optimization of Experimental Control—0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified