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 826–850 of 3073 papers

TitleStatusHype
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