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 2531–2540 of 3073 papers

Title	Date	Tasks	Status	Hype
Adversarial Sampling for Active Learning	Aug 20, 2018	Active LearningGeneral Classification	—Unverified	0
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study	Aug 16, 2018	Active LearningOpen-Ended Question Answering	—Unverified	0
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers	Aug 14, 2018	Active LearningGeneral Classification	CodeCode Available	0
OBOE: Collaborative Filtering for AutoML Model Selection	Aug 9, 2018	Active LearningAutoML	CodeCode Available	1
Active Learning for Regression Using Greedy Sampling	Aug 8, 2018	Active LearningDiversity	CodeCode Available	0
Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression	Aug 8, 2018	Active Learningregression	—Unverified	0
Active Learning based on Data Uncertainty and Model Sensitivity	Aug 6, 2018	Active LearningMetric Learning	—Unverified	0
Active Learning for Wireless IoT Intrusion Detection	Aug 4, 2018	Active LearningBIG-bench Machine Learning	—Unverified	0
Active DOP: A constituency treebank annotation tool with online learning	Aug 1, 2018	Active LearningFeature Engineering	CodeCode Available	0
The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation	Aug 1, 2018	Active LearningEntity Linking	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