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 151160 of 3073 papers

TitleStatusHype
Big Batch Bayesian Active Learning by Considering Predictive Probabilities0
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
Mechanics and Design of Metastructured Auxetic Patches with Bio-inspired Materials0
Advanced Tutorial: Label-Efficient Two-Sample Tests0
Active Learning Enables Extrapolation in Molecular Generative Models0
Bayesian Active Learning By Distribution DisagreementCode0
Joint Out-of-Distribution Filtering and Data Discovery Active Learning0
Towards Cost-Effective Learning: A Synergy of Semi-Supervised and Active Learning0
U-GIFT: Uncertainty-Guided Firewall for Toxic Speech in Few-Shot Scenario0
ACIL: Active Class Incremental Learning for Image Classification0
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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