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 2251–2260 of 3073 papers

Title	Date	Tasks	Status
Spatially regularized active diffusion learning for high-dimensional images	Nov 6, 2019	Active LearningVocal Bursts Intensity Prediction	—Unverified
Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning	Nov 1, 2019	Active Learning	—Unverified
Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts	Nov 1, 2019	Active Learning	—Unverified
Active Learning via Membership Query Synthesis for Semi-Supervised Sentence Classification	Nov 1, 2019	Active LearningGeneral Classification	CodeCode Available
Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation	Nov 1, 2019	Active LearningMachine Translation	CodeCode Available
Empirical Evaluation of Active Learning Techniques for Neural MT	Nov 1, 2019	Active LearningMachine Translation	—Unverified
Safe Exploration for Interactive Machine Learning	Oct 30, 2019	Active LearningBayesian Optimization	—Unverified
Understand customer reviews with less data and in short time: pretrained language representation and active learning	Oct 29, 2019	Active LearningBIG-bench Machine Learning	—Unverified
Small-GAN: Speeding Up GAN Training Using Core-sets	Oct 29, 2019	Active LearningAnomaly Detection	—Unverified
An Active Approach for Model Interpretation	Oct 27, 2019	Active Learningmodel	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