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 851–900 of 3073 papers

TitleStatusHype
Active Structure Learning of Bayesian Networks in an Observational SettingCode0
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network modelsCode0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?Code0
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based InferenceCode0
RareGAN: Generating Samples for Rare ClassesCode0
AutoAL: Automated Active Learning with Differentiable Query Strategy SearchCode0
Active Learning for Decision-Making from Imbalanced Observational DataCode0
Active Learning for Manifold Gaussian Process RegressionCode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
Automated discovery of a robust interatomic potential for aluminumCode0
Active Learning with Contrastive Pre-training for Facial Expression RecognitionCode0
Environmental Sensor Placement with Convolutional Gaussian Neural ProcessesCode0
Black-Box Batch Active Learning for RegressionCode0
Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal ModelingCode0
Active Learning for Deep Gaussian Process SurrogatesCode0
Automated Performance Testing Based on Active Deep LearningCode0
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized ExperimentsCode0
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
Automated wildlife image classification: An active learning tool for ecological applicationsCode0
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
Risk-Aware Active Inverse Reinforcement LearningCode0
Active Semi-Supervised Learning Using Sampling Theory for Graph SignalsCode0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
Automatic Segmentation of the Spinal Cord Nerve RootletsCode0
Sample Efficient Learning of Predictors that Complement HumansCode0
Bayesian Dark KnowledgeCode0
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active LearningCode0
ScatterShot: Interactive In-context Example Curation for Text TransformationCode0
Scoping Review of Active Learning Strategies and their Evaluation Environments for Entity Recognition TasksCode0
A Bayesian Approach for Sequence Tagging with CrowdsCode0
Self-Regulated Interactive Sequence-to-Sequence LearningCode0
Bayesian Batch Active Learning as Sparse Subset ApproximationCode0
BatchGFN: Generative Flow Networks for Batch Active LearningCode0
Active Selection of Classification FeaturesCode0
Bayesian Active Learning By Distribution DisagreementCode0
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT ImagesCode0
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active LearningCode0
Batch Decorrelation for Active Metric LearningCode0
Bayesian active learning for optimization and uncertainty quantification in protein dockingCode0
Batch Active Learning at ScaleCode0
Batch Active Learning Using Determinantal Point ProcessesCode0
Active Gradual Machine Learning for Entity ResolutionCode0
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic SegmentationCode0
Batch Active Preference-Based Learning of Reward FunctionsCode0
CFlowNets: Continuous Control with Generative Flow NetworksCode0
Cost-Sensitive Active Learning for Incomplete DataCode0
Efficient Concept Drift Handling for Batch Android Malware Detection ModelsCode0
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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