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 1151–1175 of 3073 papers

TitleStatusHype
Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints—0
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks—0
Coresets for Classification -- Simplified and Strengthened—0
Coresets for Classification – Simplified and Strengthened—0
Importance sampling based active learning for parametric seismic fragility curve estimation—0
Data-driven discovery of free-form governing differential equations—0
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions—0
Adding more data does not always help: A study in medical conversation summarization with PEGASUS—0
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification—0
Cost-Based Budget Active Learning for Deep Learning—0
Addressing Limited Data for Textual Entailment Across Domains—0
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning—0
Addressing practical challenges in Active Learning via a hybrid query strategy—0
Active Learning for Post-Editing Based Incrementally Retrained MT—0
Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Active Learning for Non-Parametric Choice Models—0
Cost-effective Variational Active Entity Resolution—0
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost—0
Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition—0
Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection—0
Cost-Sensitive Active Learning for Dialogue State Tracking—0
A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections—0
Active Learning for Product Type Ontology Enhancement in E-commerce—0
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget—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