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 776–800 of 3073 papers

TitleStatusHype
Black-Box Batch Active Learning for RegressionCode0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource LanguagesCode0
Active Keyword Selection to Track Evolving Topics on TwitterCode0
A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TARCode0
LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow RecognitionCode0
Active Learning for Neural Machine TranslationCode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted NetworksCode0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
Matching a Desired Causal State via Shift InterventionsCode0
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?Code0
Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample AssessmentCode0
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active LearningCode0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory ExplorationCode0
Bayesian active learning for optimization and uncertainty quantification in protein dockingCode0
Bayesian Active Learning By Distribution DisagreementCode0
Bayesian Batch Active Learning as Sparse Subset ApproximationCode0
ACTIVETHIEF: Model Extraction Using Active Learning and Unannotated Public DataCode0
A Simple yet Brisk and Efficient Active Learning Platform for Text ClassificationCode0
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active LearningCode0
Batch Decorrelation for Active Metric LearningCode0
BatchGFN: Generative Flow Networks for Batch Active LearningCode0
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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