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 2481–2490 of 3073 papers

Title	Date	Tasks	Status
Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization	Nov 20, 2018	Active LearningFraud Detection	—Unverified
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning	Nov 19, 2018	Active LearningQ-Learning	CodeCode Available
Deep Active Learning with a Neural Architecture Search	Nov 19, 2018	Active LearningNeural Architecture Search	CodeCode Available
SHINRA: Structuring Wikipedia by Collaborative Contribution	Nov 17, 2018	Active LearningAttribute	—Unverified
Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via Ensembles	Nov 9, 2018	Active LearningGeneral Classification	—Unverified
Active Learning using Deep Bayesian Networks for Surgical Workflow Analysis	Nov 8, 2018	Active LearningBIG-bench Machine Learning	—Unverified
Large-Scale Visual Active Learning with Deep Probabilistic Ensembles	Nov 8, 2018	Active LearningGeneral Classification	—Unverified
Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization	Nov 6, 2018	Active LearningGeneral Classification	—Unverified
Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls	Nov 5, 2018	Active LearningBIG-bench Machine Learning	—Unverified
Exploring Connections Between Active Learning and Model Extraction	Nov 5, 2018	Active LearningBIG-bench Machine Learning	—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