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 2471–2480 of 3073 papers

Title	Date	Tasks	Status
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation	Dec 1, 2018	Active LearningBayesian Optimisation	CodeCode Available
Explore-Exploit: A Framework for Interactive and Online Learning	Dec 1, 2018	Active Learning	—Unverified
Learning to Caption Images through a Lifetime by Asking Questions	Dec 1, 2018	Active LearningImage Captioning	CodeCode Available
Active Learning in Recommendation Systems with Multi-level User Preferences	Nov 30, 2018	Active LearningRecommendation Systems	—Unverified
Are All Training Examples Created Equal? An Empirical Study	Nov 30, 2018	Active LearningAll	—Unverified
The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning	Nov 29, 2018	Active LearningDeep Learning	—Unverified
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision	Nov 29, 2018	Action RecognitionActive Learning	CodeCode Available
HS^2: Active Learning over Hypergraphs	Nov 25, 2018	Active Learning	—Unverified
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence	Nov 24, 2018	Active LearningGaussian Processes	—Unverified
Robust Active Learning for Electrocardiographic Signal Classification	Nov 21, 2018	Active LearningClassification	—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