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 1026–1050 of 3073 papers

TitleStatusHype
Active Learning Applied to Patient-Adaptive Heartbeat Classification—0
Deep Active Learning for Anomaly Detection—0
A General Approach to Domain Adaptation with Applications in Astronomy—0
Active Learning for Single Neuron Models with Lipschitz Non-Linearities—0
Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery—0
A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning—0
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates—0
Active Learning and the Irish Treebank—0
Active learning for sense annotation—0
A framework for the extraction of Deep Neural Networks by leveraging public data—0
Active Learning and Proofreading for Delineation of Curvilinear Structures—0
Active learning and negative evidence for language identification—0
Extended Active Learning Method—0
Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy—0
Active Learning for Segmentation Based on Bayesian Sample Queries—0
A Finite-Horizon Approach to Active Level Set Estimation—0
Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression—0
Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy—0
Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization—0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
Comments on the proof of adaptive submodular function minimization—0
ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development—0
Active Learning for Rumor Identification on Social Media—0
Adversarial Vulnerability of Active Transfer Learning—0
Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift—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