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 21312140 of 3073 papers

TitleStatusHype
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes0
Variational Adaptive-Newton Method for Explorative Learning0
Vibration-based Uncertainty Estimation for Learning from Limited Supervision0
Video Annotation and Tracking with Active Learning0
VideoCoT: A Video Chain-of-Thought Dataset with Active Annotation Tool0
Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities0
Visual Causal Feature Learning0
Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis0
Visual Supervision in Bootstrapped Information Extraction0
Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas0
Show:102550
← PrevPage 214 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