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 226–250 of 3073 papers

TitleStatusHype
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient LearningCode1
Active learning for medical image segmentation with stochastic batchesCode1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
Learning Distinctive Margin toward Active Domain AdaptationCode1
LLMaAA: Making Large Language Models as Active AnnotatorsCode1
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language ModelsCode1
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
A Tutorial on Thompson SamplingCode1
Making Better Use of Unlabelled Data in Bayesian Active LearningCode1
Making Your First Choice: To Address Cold Start Problem in Vision Active LearningCode1
Materials Property Prediction with Uncertainty Quantification: A Benchmark StudyCode1
Memory-Based Dual Gaussian Processes for Sequential LearningCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
Model Assertions for Monitoring and Improving ML ModelsCode1
Model Learning with Personalized Interpretability Estimation (ML-PIE)Code1
Bayesian Model-Agnostic Meta-LearningCode1
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
Multi-Objective GFlowNetsCode1
Multi-task Causal Learning with Gaussian ProcessesCode1
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object DetectionCode1
Learning Loss for Active LearningCode1
Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution EquationsCode1
ActiveGLAE: A Benchmark for Deep Active Learning with TransformersCode1
AISecKG: Knowledge Graph Dataset for Cybersecurity EducationCode1
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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