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 2041–2050 of 3073 papers

Title	Date	Tasks	Status	Hype
On uncertainty estimation in active learning for image segmentation	Jul 13, 2020	Active LearningImage Segmentation	CodeCode Available	1
IALE: Imitating Active Learner Ensembles	Jul 9, 2020	Active LearningImitation Learning	CodeCode Available	0
Resource Aware Multifidelity Active Learning for Efficient Optimization	Jul 9, 2020	Active LearningBayesian Optimization	—Unverified	0
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation	Jul 9, 2020	Active LearningClassification	—Unverified	0
Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification	Jul 9, 2020	Active LearningGeneral Classification	—Unverified	0
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images	Jul 7, 2020	Active Learning	CodeCode Available	0
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction	Jul 7, 2020	Active LearningGraph Neural Network	CodeCode Available	1
Meta-active Learning in Probabilistically-Safe Optimization	Jul 7, 2020	Active LearningMeta-Learning	—Unverified	0
The Sample Complexity of Best-k Items Selection from Pairwise Comparisons	Jul 6, 2020	Active Learning	CodeCode Available	0
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design	Jul 4, 2020	Active LearningMulti-Armed Bandits	—Unverified	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