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 1931–1940 of 3073 papers

TitleStatusHype
Contrastive Coding for Active Learning Under Class Distribution Mismatch—0
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach—0
Whom to Test? Active Sampling Strategies for Managing COVID-19—0
An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification—0
Active Deep Learning on Entity Resolution by Risk Sampling—0
Self-supervised self-supervision by combining deep learning and probabilistic logic—0
Learning Halfspaces With Membership Queries—0
On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise—0
An Information-Theoretic Framework for Unifying Active Learning ProblemsCode0
Multi-fidelity Bayesian Neural Networks: Algorithms and Applications—0
Show:102550
← PrevPage 194 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