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 801–825 of 3073 papers

TitleStatusHype
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
A Bayesian Approach for Sequence Tagging with CrowdsCode0
Aspect-based Sentiment Analysis of Scientific ReviewsCode0
Active Learning for Classifying 2D Grid-Based Level CompletabilityCode0
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted NetworksCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
ACTIVETHIEF: Model Extraction Using Active Learning and Unannotated Public DataCode0
Bayesian Batch Active Learning as Sparse Subset ApproximationCode0
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active LearningCode0
Bayesian Dark KnowledgeCode0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait SketchingCode0
A Structural-Clustering Based Active Learning for Graph Neural NetworksCode0
Near-Polynomially Competitive Active Logistic RegressionCode0
Active Structure Learning of Bayesian Networks in an Observational SettingCode0
A Study of Acquisition Functions for Medical Imaging Deep Active LearningCode0
BatchGFN: Generative Flow Networks for Batch Active LearningCode0
Batch Decorrelation for Active Metric LearningCode0
Bayesian Active Learning By Distribution DisagreementCode0
Non-Parametric Calibration for ClassificationCode0
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based InferenceCode0
A Survey of Deep Active LearningCode0
Active Learning to Guide Labeling Efforts for Question Difficulty EstimationCode0
Active Learning for Manifold Gaussian Process RegressionCode0
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active LearningCode0
Show:102550
← PrevPage 33 of 123Next →

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