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 29012910 of 3073 papers

TitleStatusHype
Learning to Multi-Task by Active SamplingCode0
Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural NetworksCode0
Semi-supervised Active Learning for Video Action DetectionCode0
Fair Active LearningCode0
Fair Active LearningCode0
On the Fragility of Active Learners for Text ClassificationCode0
Fairness Without Harm: An Influence-Guided Active Sampling ApproachCode0
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and TechniquesCode0
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise TasksCode0
LSCALE: Latent Space Clustering-Based Active Learning for Node ClassificationCode0
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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