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 2431–2440 of 3073 papers

TitleStatusHype
Analyzing Data Selection Techniques with Tools from the Theory of Information Losses—0
Active learning via informed search in movement parameter space for efficient robot task learning and transferCode0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
Information Losses in Neural Classifiers from Sampling—0
K-nn active learning under local smoothness condition—0
Active Learning for High-Dimensional Binary Features—0
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning—0
Learning Linear Dynamical Systems with Semi-Parametric Least SquaresCode0
Bayesian active learning for optimization and uncertainty quantification in protein dockingCode0
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector—0
Show:102550
← PrevPage 244 of 308Next →

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