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 1451–1500 of 3073 papers

TitleStatusHype
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations—0
Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems—0
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models—0
Efficient Label Collection for Unlabeled Image Datasets—0
Efficient Learning of Linear Separators under Bounded Noise—0
An Exploration of Active Learning for Affective Digital Phenotyping—0
Efficiently labelling sequences using semi-supervised active learning—0
Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization—0
Efficient Named Entity Annotation through Pre-empting—0
Efficient Nonmyopic Active Search—0
Active Learning for New Domains in Natural Language Understanding—0
An information-matching approach to optimal experimental design and active learning—0
Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines—0
Active Learning of Convex Halfspaces on Graphs—0
Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning—0
AcTune: Uncertainty-Aware Active Self-Training for Active Fine-Tuning of Pretrained Language Models—0
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials—0
Events Beyond ACE: Curated Training for Events—0
ELAD: Explanation-Guided Large Language Models Active Distillation—0
Understanding the Eluder Dimension—0
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation—0
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning—0
Embodied Learning for Lifelong Visual Perception—0
Embodied Visual Active Learning for Semantic Segmentation—0
Empirical Evaluation of Active Learning Techniques for Neural MT—0
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