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 2976–3000 of 3073 papers

TitleStatusHype
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc ExplainabilityCode0
Foundation Model Makes Clustering A Better Initialization For Cold-Start Active LearningCode0
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transientsCode0
Using active learning to expand training data for implicit discourse relation recognitionCode0
Automated wildlife image classification: An active learning tool for ecological applicationsCode0
Optimal Bayesian Affine Estimator and Active Learning for the Wiener ModelCode0
Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic ActuatorsCode0
Regional based query in graph active learningCode0
Automated Seed Quality Testing System using GAN & Active LearningCode0
Toward Optimal Probabilistic Active Learning Using a Bayesian ApproachCode0
Active learning in annotating micro-blogs dealing with e-reputationCode0
Active Learning-Based Species Range EstimationCode0
Low Rank Learning for Offline Query OptimizationCode0
Active Learning amidst Logical KnowledgeCode0
From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active LearningCode0
Frugal Algorithm SelectionCode0
Active Learning for Entity Filtering in Microblog StreamsCode0
Active Learning for Entity AlignmentCode0
LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model TrainingCode0
Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunitiesCode0
LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow RecognitionCode0
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity RecognizersCode0
LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus ImagesCode0
Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image ClassificationCode0
GALAXY: Graph-based Active Learning at the ExtremeCode0
Show:102550
← PrevPage 120 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