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 1351–1400 of 3073 papers

TitleStatusHype
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learningCode0
Nearest Neighbor Classifier with Margin Penalty for Active LearningCode0
A Framework and Benchmark for Deep Batch Active Learning for RegressionCode1
Multilingual Detection of Personal Employment Status on TwitterCode0
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
Uncertainty Estimation for Language Reward Models—0
Active Learning by Feature MixingCode1
A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about FluidsCode0
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering—0
Learning Distinctive Margin toward Active Domain AdaptationCode1
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
Onception: Active Learning with Expert Advice for Real World Machine TranslationCode0
Reinforced Meta Active Learning—0
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
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning—0
Modulation and signal class labelling using active learning and classification using machine learning—0
Parallel MCMC Without Embarrassing FailuresCode0
t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning—0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets—0
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks—0
Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs—0
FAMIE: A Fast Active Learning Framework for Multilingual Information ExtractionCode1
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model EvaluationCode1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic SegmentationCode1
Double-Barreled Question Detection at Momentive—0
Fast Rates in Pool-Based Batch Active Learning—0
Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning—0
Sampling Strategy for Fine-Tuning Segmentation Models to Crisis Area under Scarcity of Data—0
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP—0
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte CarloCode1
Improving greedy core-set configurations for active learning with uncertainty-scaled distances—0
A Lagrangian Duality Approach to Active Learning—0
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation—0
Improving Probabilistic Models in Text Classification via Active Learning—0
Active metric learning and classification using similarity queries—0
Ranking with Confidence for Large Scale Comparison DataCode0
GALAXY: Graph-based Active Learning at the ExtremeCode0
Active Multi-Task Representation Learning—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