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 1421–1430 of 3073 papers

TitleStatusHype
Distributionally Robust Active Learning for Gaussian Process Regression—0
Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images—0
Distributional Latent Variable Models with an Application in Active Cognitive Testing—0
Active Learning in Incomplete Label Multiple Instance Multiple Label Learning—0
Information-Theoretic Active Correlation Clustering—0
Effective Data Selection for Seismic Interpretation through Disagreement—0
Active Community Detection with Maximal Expected Model Change—0
Effective Version Space Reduction for Convolutional Neural Networks—0
ACIL: Active Class Incremental Learning for Image Classification—0
Distributed Safe Learning and Planning for Multi-robot Systems—0
Show:102550
← PrevPage 143 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