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 51–75 of 3073 papers

TitleStatusHype
Active Testing: Sample-Efficient Model EvaluationCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
Active WeaSuL: Improving Weak Supervision with Active LearningCode1
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language ModelsCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center DatasetCode1
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experimentsCode1
A Framework and Benchmark for Deep Batch Active Learning for RegressionCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
A Holistic Approach to Undesired Content Detection in the Real WorldCode1
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
AL-GTD: Deep Active Learning for Gaze Target DetectionCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
Active Learning from the WebCode1
Active Learning Meets Optimized Item SelectionCode1
Active Learning Through a Covering LensCode1
ActiveNeRF: Learning where to See with Uncertainty EstimationCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
Active learning for medical image segmentation with stochastic batchesCode1
Active Anomaly Detection via EnsemblesCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
Active Learning for Coreference Resolution using Discrete AnnotationCode1
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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