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 51–100 of 3073 papers

TitleStatusHype
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
Stochastic Batch Acquisition: A Simple Baseline for Deep Active LearningCode1
Open Source Software for Efficient and Transparent ReviewsCode1
AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active LearningCode1
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experimentsCode1
A Tutorial on Thompson SamplingCode1
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
Bayesian active learning for production, a systematic study and a reusable libraryCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Bayesian Model-Agnostic Meta-LearningCode1
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science DomainsCode1
BenchPress: A Deep Active Benchmark GeneratorCode1
Active WeaSuL: Improving Weak Supervision with Active LearningCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language ModelsCode1
A Framework and Benchmark for Deep Batch Active Learning for RegressionCode1
Active Sensing for Communications by LearningCode1
ActiveNeRF: Learning where to See with Uncertainty EstimationCode1
Active Statistical InferenceCode1
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-TrainingCode1
Active Learning Through a Covering LensCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model EvaluationCode1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
Active Learning from the WebCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
Active Bayesian Causal InferenceCode1
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
Active learning with MaskAL reduces annotation effort for training Mask R-CNNCode1
Active Learning for Open-set AnnotationCode1
Active Pointly-Supervised Instance SegmentationCode1
Active Prompt Learning in Vision Language ModelsCode1
Active Learning Meets Optimized Item SelectionCode1
Active Anomaly Detection via EnsemblesCode1
Active Testing: Sample-Efficient Model EvaluationCode1
A Comparative Survey of Deep Active LearningCode1
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain KnowledgeCode1
Adaptive Superpixel for Active Learning in Semantic SegmentationCode1
A deep active learning system for species identification and counting in camera trap imagesCode1
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center DatasetCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
AISecKG: Knowledge Graph Dataset for Cybersecurity EducationCode1
AL-GTD: Deep Active Learning for Gaze Target DetectionCode1
A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain KnowledgeCode1
A Mathematical Analysis of Learning Loss for Active Learning in RegressionCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
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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