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 1771–1780 of 3073 papers

TitleStatusHype
Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data—0
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems—0
Query Complexity of Active Learning for Function Family With Nearly Orthogonal Basis—0
Query-Efficient Black-Box Attack by Active Learning—0
Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning—0
Radar Anti-jamming Strategy Learning via Domain-knowledge Enhanced Online Convex Optimization—0
RadGrad: Active learning with loss gradients—0
Radically Lower Data-Labeling Costs for Visually Rich Document Extraction Models—0
RAFT: Robust Augmentation of FeaTures for Image Segmentation—0
Railway LiDAR semantic segmentation based on intelligent semi-automated data annotation—0
Show:102550
← PrevPage 178 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