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 1651–1660 of 3073 papers

TitleStatusHype
Open-/Closed-loop Active Learning for Data-driven Predictive Control—0
Open Long-Tailed Recognition in a Dynamic World—0
Open-World Active Learning with Stacking Ensemble for Self-Driving Cars—0
Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms—0
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up—0
Optimal and Efficient Binary Questioning for Human-in-the-Loop Annotation—0
Optimal Data Set Selection: An Application to Grapheme-to-Phoneme Conversion—0
Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes—0
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks—0
Σ-Optimality for Active Learning on Gaussian Random Fields—0
Show:102550
← PrevPage 166 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