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 2881–2890 of 3073 papers

Title	Date	Tasks	Status
Learning Salient Samples and Distributed Representations for Topic-Based Chinese Message Polarity Classification	Jul 1, 2015	Active LearningGeneral Classification	—Unverified
Combining Active Learning and Partial Annotation for Domain Adaptation of a Japanese Dependency Parser	Jul 1, 2015	Active LearningDependency Parsing	—Unverified
Can Natural Language Processing Become Natural Language Coaching?	Jul 1, 2015	Active LearningGrammatical Error Detection	—Unverified
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification	Jun 29, 2015	Active LearningGeneral Classification	—Unverified
Efficient and Parsimonious Agnostic Active Learning	Jun 29, 2015	Active LearningGeneral Classification	—Unverified
Bayesian Dark Knowledge	Jun 14, 2015	Active Learningparameter estimation	CodeCode Available
Convergence Rates of Active Learning for Maximum Likelihood Estimation	Jun 8, 2015	Active LearningBinary Classification	—Unverified
Strat\'egies de s\'election des exemples pour l'apprentissage actif avec des champs al\'eatoires conditionnels	Jun 1, 2015	Active LearningChunking	—Unverified
An Analytic and Empirical Evaluation of Return-on-Investment-Based Active Learning	Jun 1, 2015	Active Learning	—Unverified
Judging the Quality of Automatically Generated Gap-fill Question using Active Learning	Jun 1, 2015	Active LearningReading Comprehension	—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