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 1751–1760 of 3073 papers

Title	Date	Tasks	Status	Hype
AdaptiFont: Increasing Individuals' Reading Speed with a Generative Font Model and Bayesian Optimization	Apr 21, 2021	Active LearningBayesian Optimization	—Unverified	0
Active and sparse methods in smoothed model checking	Apr 20, 2021	Active Learningmodel	—Unverified	0
LaTeX-Numeric: Language-agnostic Text attribute eXtraction for E-commerce Numeric Attributes	Apr 19, 2021	Active LearningAttribute	—Unverified	0
A Mathematical Analysis of Learning Loss for Active Learning in Regression	Apr 19, 2021	Active LearningPose Estimation	CodeCode Available	1
Bayesian Uncertainty and Expected Gradient Length -- Regression: Two Sides Of The Same Coin?	Apr 19, 2021	Active LearningPose Estimation	CodeCode Available	1
Data Shapley Valuation for Efficient Batch Active Learning	Apr 16, 2021	Active Learning	—Unverified	0
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning	Apr 16, 2021	Active LearningNatural Language Understanding	CodeCode Available	1
Learning User's confidence for active learning	Apr 15, 2021	Active LearningPansharpening	—Unverified	0
A survey of active learning algorithms for supervised remote sensing image classification	Apr 15, 2021	Active LearningGeneral Classification	—Unverified	0
Personalized Semi-Supervised Federated Learning for Human Activity Recognition	Apr 15, 2021	Active LearningActivity Recognition	—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