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 1476–1500 of 3073 papers

TitleStatusHype
Empirical Evaluations of Active Learning Strategies in Legal Document Review—0
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
Empowering Language Models with Active Inquiry for Deeper Understanding—0
Enhanced Labelling in Active Learning for Coreference Resolution—0
Active and passive learning of linear separators under log-concave distributions—0
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables—0
Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation—0
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models—0
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders—0
Enhancing Modality Representation and Alignment for Multimodal Cold-start Active Learning—0
Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls—0
Comprehensive Benchmarking of Entropy and Margin Based Scoring Metrics for Data Selection—0
Active Learning of General Halfspaces: Label Queries vs Membership Queries—0
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation—0
Anomaly Detection in Hierarchical Data Streams under Unknown Models—0
Compositional Active Learning of Synchronizing Systems through Automated Alphabet Refinement—0
Minimum-Margin Active Learning—0
Enhancing the Efficiency of Complex Systems Crystal Structure Prediction by Active Learning Guided Machine Learning Potential—0
Active Learning for Accurate Estimation of Linear Models—0
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification—0
Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing—0
A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis—0
Entity Matching by Pool-based Active Learning—0
Accelerating Battery Material Optimization through iterative Machine Learning—0
Evidential uncertainties on rich labels for active learning—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