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 1171–1180 of 3073 papers

TitleStatusHype
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
CoTAL: Human-in-the-Loop Prompt Engineering, Chain-of-Thought Reasoning, and Active Learning for Generalizable Formative Assessment Scoring—0
Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseases—0
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget—0
A domain-decomposed VAE method for Bayesian inverse problems—0
CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation—0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—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