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 691–700 of 3073 papers

TitleStatusHype
A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction—0
Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments—0
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic SegmentationCode0
Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints—0
Aligning Data Selection with Performance: Performance-driven Reinforcement Learning for Active Learning in Object Detection—0
Refined Mechanism Design for Approximately Structured Priors via Active Regression—0
Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize—0
Data efficient deep learning for medical image analysis: A survey—0
Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing—0
High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation—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