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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 3021–3030 of 3073 papers

Title	Date	Tasks	Status
Online allocation and homogeneous partitioning for piecewise constant mean-approximation	Dec 1, 2012	Active Learning	—Unverified
Bayesian active learning with localized priors for fast receptive field characterization	Dec 1, 2012	Active Learning	—Unverified
Hierarchical Optimistic Region Selection driven by Curiosity	Dec 1, 2012	Active LearningMulti-Armed Bandits	—Unverified
A Linear Time Active Learning Algorithm for Link Classification	Dec 1, 2012	Active LearningClassification	—Unverified
Collaborative Gaussian Processes for Preference Learning	Dec 1, 2012	Active LearningDimensionality Reduction	—Unverified
Active Learning of Multi-Index Function Models	Dec 1, 2012	Active Learning	—Unverified
Multilabel Classification using Bayesian Compressed Sensing	Dec 1, 2012	Active LearningClassification	—Unverified
Active Learning of Model Evidence Using Bayesian Quadrature	Dec 1, 2012	Active LearningAstronomy	—Unverified
Active and passive learning of linear separators under log-concave distributions	Nov 6, 2012	Active LearningOpen-Ended Question Answering	—Unverified
Active Learning for Crowd-Sourced Databases	Sep 17, 2012	Active LearningBIG-bench Machine Learning	—Unverified

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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