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 241250 of 3073 papers

TitleStatusHype
Model Learning with Personalized Interpretability Estimation (ML-PIE)Code1
Bayesian Model-Agnostic Meta-LearningCode1
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
Multi-Objective GFlowNetsCode1
Multi-task Causal Learning with Gaussian ProcessesCode1
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object DetectionCode1
Learning Loss for Active LearningCode1
Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution EquationsCode1
ActiveGLAE: A Benchmark for Deep Active Learning with TransformersCode1
AISecKG: Knowledge Graph Dataset for Cybersecurity EducationCode1
Show:102550
← PrevPage 25 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