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 1126–1150 of 3073 papers

TitleStatusHype
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions—0
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint—0
Constructing a Korean Named Entity Recognition Dataset for the Financial Domain using Active Learning—0
Containing a spread through sequential learning: to exploit or to explore?—0
Context Aware Active Learning of Activity Recognition Models—0
Context-aware Active Multi-Step Reinforcement Learning—0
Context Aware Image Annotation in Active Learning—0
Context-Aware Query Selection for Active Learning in Event Recognition—0
Context-driven Active and Incremental Activity Recognition—0
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification—0
Active Learning for Non-Parametric Choice Models—0
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost—0
Multi-View Active Learning for Short Text Classification in User-Generated Data—0
Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition—0
Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties—0
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget—0
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions—0
Continuous Active Learning Using Pretrained Transformers—0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—0
Contrastive Coding for Active Learning Under Class Distribution Mismatch—0
Adaptive Submodular Ranking and Routing—0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning—0
Convergence Rates of Active Learning for Maximum Likelihood Estimation—0
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition—0
Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax—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