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 1351–1375 of 3073 papers

TitleStatusHype
Representative Subset Selection for Efficient Fine-Tuning in Self-Supervised Speech Recognition—0
Multilingual Detection of Personal Employment Status on TwitterCode0
A Framework and Benchmark for Deep Batch Active Learning for RegressionCode1
Nearest Neighbor Classifier with Margin Penalty for Active LearningCode0
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
Uncertainty Estimation for Language Reward Models—0
Active Learning by Feature MixingCode1
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering—0
Learning Distinctive Margin toward Active Domain AdaptationCode1
A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about FluidsCode0
Can I see an Example? Active Learning the Long Tail of Attributes and Relations—0
BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets—0
Optical Flow Training under Limited Label Budget via Active LearningCode1
Reinforced Meta Active Learning—0
Onception: Active Learning with Expert Advice for Real World Machine TranslationCode0
Active Self-Semi-Supervised Learning for Few Labeled Samples—0
Boosting the Learning for Ranking Patterns—0
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active AnnotationCode1
Passive and Active Learning of Driver Behavior from Electric Vehicles—0
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions—0
Biological Sequence Design with GFlowNetsCode1
Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution EquationsCode1
Information Gain Propagation: a new way to Graph Active Learning with Soft LabelsCode1
Active learning with binary models for real time data labelling—0
Bayesian Active Learning for Discrete Latent Variable Models—0
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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