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

Showing 17111720 of 3073 papers

TitleStatusHype
Active Learning for Assisted Corpus Construction: A Case Study in Knowledge Discovery from Biomedical Text0
Headnote Prediction Using Machine Learning0
TAR on Social Media: A Framework for Online Content ModerationCode0
Certifying One-Phase Technology-Assisted Reviews0
Reducing Label Effort: Self-Supervised meets Active Learning0
Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays0
Region-level Active Detector Learning0
Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil0
Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning0
Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation0
Show:102550
← PrevPage 172 of 308Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified