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 2771–2780 of 3073 papers

Title	Date	Tasks	Status
Budgeted stream-based active learning via adaptive submodular maximization	Dec 1, 2016	Active Learning	—Unverified
Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax	Dec 1, 2016	Active LearningNatural Language Inference	—Unverified
TextPro-AL: An Active Learning Platform for Flexible and Efficient Production of Training Data for NLP Tasks	Dec 1, 2016	Active LearningBIG-bench Machine Learning	—Unverified
Zero-resource Dependency Parsing: Boosting Delexicalized Cross-lingual Transfer with Linguistic Knowledge	Dec 1, 2016	Active LearningCross-Lingual Transfer	—Unverified
Challenges and Solutions for Latin Named Entity Recognition	Dec 1, 2016	Active LearningDomain Adaptation	—Unverified
Align Me: A framework to generate Parallel Corpus Using OCRs and Bilingual Dictionaries	Dec 1, 2016	Active LearningInformation Retrieval	—Unverified
Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary	Dec 1, 2016	Active LearningAutomatic Speech Recognition	—Unverified
Active learning for detection of stance components	Dec 1, 2016	Active LearningOpinion Mining	—Unverified
Active Deep Learning for Classification of Hyperspectral Images	Nov 30, 2016	Active LearningClassification	—Unverified
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning	Nov 29, 2016	Active LearningMulti-Label Classification	CodeCode Available

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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