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 2751–2775 of 3073 papers

TitleStatusHype
Active model learning and diverse action sampling for task and motion planningCode0
Investigating Multi-source Active Learning for Natural Language InferenceCode0
Active ML for 6G: Towards Efficient Data Generation, Acquisition, and AnnotationCode0
Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text DatasetsCode0
A Bibliographic View on Constrained ClusteringCode0
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class ClassifiersCode0
Actively Learning Gaussian Process DynamicsCode0
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT SegmentationCode0
Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active LearningCode0
Neural Predictive Monitoring under Partial ObservabilityCode0
Eeny, meeny, miny, moe. How to choose data for morphological inflectionCode0
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active LearningCode0
Scoping Review of Active Learning Strategies and their Evaluation Environments for Entity Recognition TasksCode0
Annotator-Centric Active Learning for Subjective NLP TasksCode0
Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable ModelsCode0
Efficacy of Bayesian Neural Networks in Active LearningCode0
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer VisionCode0
Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networksCode0
An Interactive Visualization Tool for Understanding Active LearningCode0
NewsPanda: Media Monitoring for Timely Conservation ActionCode0
Active Learning for Decision-Making from Imbalanced Observational DataCode0
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costsCode0
Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input SpaceCode0
STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional SettingsCode0
The Right Tool for the Job: Matching Active Learning Techniques to Learning ObjectivesCode0
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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