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
Open Source Software for Efficient and Transparent ReviewsCode1
AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active LearningCode1
A Survey of Dataset Refinement for Problems in Computer Vision DatasetsCode1
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language ModelsCode1
A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain KnowledgeCode1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
On the Importance of Effectively Adapting Pretrained Language Models for Active LearningCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Active Learning Meets Optimized Item SelectionCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
BenchPress: A Deep Active Benchmark GeneratorCode1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
A Framework and Benchmark for Deep Batch Active Learning for RegressionCode1
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation EffortsCode1
All you need are a few pixels: semantic segmentation with PixelPickCode1
A deep active learning system for species identification and counting in camera trap imagesCode1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic SegmentationCode1
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center DatasetCode1
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language ModelsCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
Adaptive Superpixel for Active Learning in Semantic SegmentationCode1
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacksCode1
ALPBench: A Benchmark for Active Learning Pipelines on Tabular DataCode1
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model EvaluationCode1
Active Statistical InferenceCode1
Active Testing: Sample-Efficient Model EvaluationCode1
Active Bayesian Causal InferenceCode1
Active Learning for Coreference Resolution using Discrete AnnotationCode1
Active learning based generative design for the discovery of wide bandgap materialsCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Active Learning by Feature MixingCode1
Active Learning for Bayesian 3D Hand Pose EstimationCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
Active Learning for BERT: An Empirical StudyCode1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experimentsCode1
A Comparative Survey of Deep Active LearningCode1
Active Learning for Deep Object Detection via Probabilistic ModelingCode1
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain KnowledgeCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
AISecKG: Knowledge Graph Dataset for Cybersecurity EducationCode1
Active Sensing for Communications by LearningCode1
Active learning for medical image segmentation with stochastic batchesCode1
Active Learning for Open-set AnnotationCode1
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced DatasetsCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
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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