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 2701–2750 of 3073 papers

TitleStatusHype
Active Semi-Supervised Learning Using Sampling Theory for Graph SignalsCode0
Active Selection of Classification FeaturesCode0
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?Code0
Disentanglement based Active LearningCode0
MyriadAL: Active Few Shot Learning for HistopathologyCode0
Dissimilar Nodes Improve Graph Active LearningCode0
DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software EcosystemCode0
Integrating Deep Metric Learning with Coreset for Active Learning in 3D SegmentationCode0
SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and DetectionCode0
An Adversarial Objective for Scalable ExplorationCode0
Preference-based Interactive Multi-Document SummarisationCode0
Distributional Gradient Matching for Learning Uncertain Neural Dynamics ModelsCode0
Active Preference Learning for Ordering Items In- and Out-of-sampleCode0
Stopping Criterion for Active Learning Based on Error StabilityCode0
Nearest Neighbor Classifier with Margin Penalty for Active LearningCode0
Interactively Teaching an Inverse Reinforcement Learner with Limited FeedbackCode0
The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive AnnotationCode0
Near-Optimal Active Learning of Multi-Output Gaussian ProcessesCode0
Distribution Discrepancy and Feature Heterogeneity for Active 3D Object DetectionCode0
Interactive Refinement of Cross-Lingual Word EmbeddingsCode0
Active Few-Shot Learning with FASLCode0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
Diversity-Aware Batch Active Learning for Dependency ParsingCode0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model BiasCode0
The Label Complexity of Active Learning from Observational DataCode0
Prioritizing Informative Features and Examples for Deep Learning from Noisy DataCode0
Domain Adaptation from ScratchCode0
Stream-based Active Learning with Verification Latency in Non-stationary EnvironmentsCode0
Interpretable Active LearningCode0
Adaptive Batch Sizes for Active Learning A Probabilistic Numerics ApproachCode0
Domain-independent Extraction of Scientific Concepts from Research ArticlesCode0
Scalable Batch Acquisition for Deep Bayesian Active LearningCode0
Near-Polynomially Competitive Active Logistic RegressionCode0
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task HierarchyCode0
Double Q-PID algorithm for mobile robot controlCode0
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random SelectionCode0
Introspective Learning : A Two-Stage Approach for Inference in Neural NetworksCode0
Anytime Active LearningCode0
DQI: Measuring Data Quality in NLPCode0
Uncertainty Estimation of Transformer Predictions for Misclassification DetectionCode0
Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier DetectionCode0
DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image SegmentationCode0
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and SolutionsCode0
Dual Active Sampling on Batch-Incremental Active LearningCode0
Neural Active Learning on Heteroskedastic DistributionsCode0
ScatterShot: Interactive In-context Example Curation for Text TransformationCode0
DUAL: Diversity and Uncertainty Active Learning for Text SummarizationCode0
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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