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 2531–2540 of 3073 papers

Title	Date	Tasks	Status
Bayesian Semi-supervised Learning with Graph Gaussian Processes	Sep 12, 2018	Active LearningGaussian Processes	CodeCode Available
Learning Time Dependent Choice	Sep 10, 2018	Active LearningPAC learning	—Unverified
Physics-Information-Aided Kriging: Constructing Covariance Functions using Stochastic Simulation Models	Sep 10, 2018	Active LearningGPR	—Unverified
Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection	Sep 8, 2018	Active LearningComputed Tomography (CT)	—Unverified
Information-Theoretic Active Learning for Content-Based Image Retrieval	Sep 7, 2018	Active LearningBinary Classification	CodeCode Available
What do I Annotate Next? An Empirical Study of Active Learning for Action Localization	Sep 1, 2018	Action LocalizationActive Learning	—Unverified
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning	Aug 29, 2018	Active Learningreinforcement-learning	CodeCode Available
Learning a Policy for Opportunistic Active Learning	Aug 29, 2018	Active LearningObject	—Unverified
Adversarial Sampling for Active Learning	Aug 20, 2018	Active LearningGeneral Classification	—Unverified
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study	Aug 16, 2018	Active LearningOpen-Ended Question Answering	—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