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 2441–2450 of 3073 papers

Title	Date	Tasks	Status	Hype
Active learning for binary classification with variable selection	Jan 29, 2019	Active LearningBinary Classification	—Unverified	0
Limitations of Assessing Active Learning Performance at Runtime	Jan 29, 2019	Active LearningGeneral Classification	—Unverified	0
Stopping Active Learning based on Predicted Change of F Measure for Text Classification	Jan 26, 2019	Active LearningGeneral Classification	—Unverified	0
The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification	Jan 26, 2019	Active LearningGeneral Classification	—Unverified	0
What does the free energy principle tell us about the brain?	Jan 23, 2019	Active LearningBayesian Inference	—Unverified	0
Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz	Jan 23, 2019	Active LearningAssortment Optimization	—Unverified	0
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning	Jan 23, 2019	Active LearningAnomaly Detection	CodeCode Available	1
Active Learning with Gaussian Processes for High Throughput Phenotyping	Jan 21, 2019	Active LearningGaussian Processes	CodeCode Available	0
Diverse mini-batch Active Learning	Jan 17, 2019	Active LearningClustering	—Unverified	0
ALiPy: Active Learning in Python	Jan 12, 2019	Active Learning	CodeCode Available	0

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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