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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 1641–1650 of 3073 papers

Title	Date	Tasks	Status	Hype
Robust and Active Learning for Deep Neural Network Regression	Jul 28, 2021	Active Learningregression	—Unverified	0
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning	Jul 27, 2021	Active LearningDeep Learning	—Unverified	0
ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation	Jul 25, 2021	Active LearningDeep Learning	CodeCode Available	1
Restless Bandits with Many Arms: Beating the Central Limit Theorem	Jul 25, 2021	Active LearningManagement	—Unverified	0
MCDAL: Maximum Classifier Discrepancy for Active Learning	Jul 23, 2021	Active LearningClassification	CodeCode Available	0
Robust Adaptive Submodular Maximization	Jul 23, 2021	Active LearningDecision Making	—Unverified	0
Active Learning in Incomplete Label Multiple Instance Multiple Label Learning	Jul 22, 2021	Active Learning	—Unverified	0
Small-Text: Active Learning for Text Classification in Python	Jul 21, 2021	Active LearningClassification	—Unverified	0
Offline Preference-Based Apprenticeship Learning	Jul 20, 2021	Active LearningOffline RL	—Unverified	0
Fully Automated Machine Learning Pipeline for Echocardiogram Segmentation	Jul 18, 2021	Active LearningBIG-bench Machine Learning	—Unverified	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