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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2621–2630 of 3073 papers

TitleStatusHype
Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift—0
Adversarial Vulnerability of Active Transfer Learning—0
ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development—0
Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression—0
A Finite-Horizon Approach to Active Level Set Estimation—0
A framework for the extraction of Deep Neural Networks by leveraging public data—0
A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning—0
Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery—0
A General Approach to Domain Adaptation with Applications in Astronomy—0
Deep Active Learning for Anomaly Detection—0
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Benchmark Results

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