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 1521–1530 of 3073 papers

TitleStatusHype
Passive and Active Learning of Driver Behavior from Electric Vehicles—0
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions—0
Active learning with binary models for real time data labelling—0
Bayesian Active Learning for Discrete Latent Variable Models—0
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning—0
Modulation and signal class labelling using active learning and classification using machine learning—0
Parallel MCMC Without Embarrassing FailuresCode0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets—0
t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning—0
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks—0
Show:102550
← PrevPage 153 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