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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 941–950 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistency	Mar 15, 2023	Active LearningComputational Efficiency	—Unverified	0
Bi-directional personalization reinforcement learning-based architecture with active learning using a multi-model data service for the travel nursing industry	Mar 14, 2023	Active Learningreinforcement-learning	—Unverified	0
Statistical Hardware Design With Multi-model Active Learning	Mar 14, 2023	Active Learningmodel	—Unverified	0
ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learning	Mar 11, 2023	3D Object DetectionActive Learning	CodeCode Available	1
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection	Mar 10, 2023	3D Object DetectionActive Learning	—Unverified	0
Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature	Mar 9, 2023	Active LearningBayesian Inference	CodeCode Available	0
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images	Mar 9, 2023	Active LearningDiagnostic	—Unverified	0
Embodied Active Learning of Relational State Abstractions for Bilevel Planning	Mar 8, 2023	Active LearningInformativeness	—Unverified	0
Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles	Mar 8, 2023	Active LearningAutonomous Vehicles	—Unverified	0
Disambiguation of Company names via Deep Recurrent Networks	Mar 7, 2023	Active LearningEntity Disambiguation	CodeCode Available	0

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TypiClust	Accuracy	93.2	—	Unverified
2	PT4AL	Accuracy	93.1	—	Unverified
3	Learning loss	Accuracy	91.01	—	Unverified
4	CoreGCN	Accuracy	90.7	—	Unverified
5	Core-set	Accuracy	89.92	—	Unverified
6	Random Baseline (Resnet18)	Accuracy	88.45	—	Unverified
7	Random Baseline (VGG16)	Accuracy	85.09	—	Unverified