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 2751–2800 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
Noisy Natural Gradient as Variational InferenceCode0
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational modelsCode0
Active Learning Using Uncertainty InformationCode0
The Sample Complexity of Best-k Items Selection from Pairwise ComparisonsCode0
Non-Parametric Calibration for ClassificationCode0
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular SimulationCode0
Efficient Classification with Counterfactual Reasoning and Active LearningCode0
Key Patch Proposer: Key Patches Contain Rich InformationCode0
Efficient Concept Drift Handling for Batch Android Malware Detection ModelsCode0
Nonstationary data stream classification with online active learning and siamese neural networksCode0
Non-Uniform Subset Selection for Active Learning in Structured DataCode0
An Information-Theoretic Framework for Unifying Active Learning ProblemsCode0
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty ModelingCode0
An Information Retrieval Approach to Building Datasets for Hate Speech DetectionCode0
Efficient Human-in-the-loop System for Guiding DNNs AttentionCode0
Knowledge-driven Active LearningCode0
ActiveEA: Active Learning for Neural Entity AlignmentCode0
Efficiently Computable Safety Bounds for Gaussian Processes in Active LearningCode0
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar DatasetsCode0
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted NetworksCode0
Label a Herd in Minutes: Individual Holstein-Friesian Cattle IdentificationCode0
An Atomistic Machine Learning Package for Surface Science and CatalysisCode0
A Bayesian Approach for Sequence Tagging with CrowdsCode0
Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop TrainingCode0
Progress & Compress: A scalable framework for continual learningCode0
Show:102550
← PrevPage 56 of 62Next →

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