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 2071–2080 of 3073 papers

TitleStatusHype
Is there something I'm missing? Topic Modeling in eDiscovery—0
On Deep Unsupervised Active Learning—0
Deep Active Learning for Solvability Prediction in Power Systems—0
Fast active learning for pure exploration in reinforcement learning—0
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking—0
Deep Active Learning by Model Interpretability—0
MetAL: Active Semi-Supervised Learning on Graphs via Meta LearningCode0
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations—0
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning—0
Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios—0
Show:102550
← PrevPage 208 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