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 801–850 of 3073 papers

TitleStatusHype
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active LearningCode0
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network modelsCode0
Active Learning for Classifying 2D Grid-Based Level CompletabilityCode0
Building a comprehensive syntactic and semantic corpus of Chinese clinical textsCode0
CAMAL: Optimizing LSM-trees via Active LearningCode0
Active Classification with Uncertainty Comparison QueriesCode0
Constraining the Parameters of High-Dimensional Models with Active LearningCode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
Neural Active Learning on Heteroskedastic DistributionsCode0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
A Structural-Clustering Based Active Learning for Graph Neural NetworksCode0
Neural Predictive Monitoring under Partial ObservabilityCode0
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?Code0
A Study of Acquisition Functions for Medical Imaging Deep Active LearningCode0
ACTIVETHIEF: Model Extraction Using Active Learning and Unannotated Public DataCode0
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted NetworksCode0
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
Bidirectional Uncertainty-Based Active Learning for Open Set AnnotationCode0
Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait SketchingCode0
Active Structure Learning of Bayesian Networks in an Observational SettingCode0
Active Learning to Guide Labeling Efforts for Question Difficulty EstimationCode0
Onception: Active Learning with Expert Advice for Real World Machine TranslationCode0
On Efficiently Acquiring Annotations for Multilingual ModelsCode0
On Graph Neural Network Ensembles for Large-Scale Molecular Property PredictionCode0
Bayesian Dark KnowledgeCode0
A Survey on Deep Learning of Small Sample in Biomedical Image AnalysisCode0
A Survey on Multi-Task LearningCode0
On the Convergence of Loss and Uncertainty-based Active Learning AlgorithmsCode0
A Bayesian Approach for Sequence Tagging with CrowdsCode0
Bayesian Batch Active Learning as Sparse Subset ApproximationCode0
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]Code0
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based InferenceCode0
Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral ImagesCode0
Overcoming Overconfidence for Active LearningCode0
Active Learning for Manifold Gaussian Process RegressionCode0
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active LearningCode0
Active Learning Using Uncertainty InformationCode0
atTRACTive: Semi-automatic white matter tract segmentation using active learningCode0
Partition-Based Active Learning for Graph Neural NetworksCode0
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active LearningCode0
Batch Decorrelation for Active Metric LearningCode0
Batch Active Learning Using Determinantal Point ProcessesCode0
Active learning via informed search in movement parameter space for efficient robot task learning and transferCode0
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and ClassificationCode0
Active Semi-Supervised Learning Using Sampling Theory for Graph SignalsCode0
Batch Active Preference-Based Learning of Reward FunctionsCode0
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with ApplicationsCode0
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