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

Showing 876900 of 3073 papers

TitleStatusHype
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
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified