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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 2961–2970 of 3073 papers

Title	Date	Tasks	Status
Active Semi-Supervised Learning Using Sampling Theory for Graph Signals	May 16, 2014	Active Learning	CodeCode Available
Focusing Annotation for Semantic Role Labeling	May 1, 2014	Active LearningDomain Adaptation	—Unverified
A Quality-based Active Sample Selection Strategy for Statistical Machine Translation	May 1, 2014	Active LearningMachine Translation	—Unverified
Language Resource Addition: Dictionary or Corpus?	May 1, 2014	Active LearningDomain Adaptation	—Unverified
Active Learning for Undirected Graphical Model Selection	Apr 13, 2014	Active Learningmodel	—Unverified
A Compression Technique for Analyzing Disagreement-Based Active Learning	Apr 5, 2014	Active Learning	—Unverified
Confidence-based Active Learning Methods for Machine Translation	Apr 1, 2014	Active LearningDomain Adaptation	—Unverified
Domain Adaptation with Active Learning for Coreference Resolution	Apr 1, 2014	Active Learningcoreference-resolution	—Unverified
Active Learning for Post-Editing Based Incrementally Retrained MT	Apr 1, 2014	Active LearningDomain Adaptation	—Unverified
Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction	Mar 6, 2014	Active 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