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 1531–1540 of 3073 papers

Title	Date	Tasks	Status	Hype
Knowledge-driven Active Learning	Oct 15, 2021	Active LearningMulti-Label Classification	CodeCode Available	0
Streaming Machine Learning and Online Active Learning for Automated Visual Inspection	Oct 15, 2021	Active LearningBIG-bench Machine Learning	—Unverified	0
An active learning approach for improving the performance of equilibrium based chemical simulations	Oct 15, 2021	Active Learning	—Unverified	0
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images	Oct 15, 2021	Active LearningLand Cover Classification	CodeCode Available	1
Model-Change Active Learning in Graph-Based Semi-Supervised Learning	Oct 14, 2021	Active Learning	CodeCode Available	1
ActiveEA: Active Learning for Neural Entity Alignment	Oct 13, 2021	Active LearningEntity Alignment	CodeCode Available	0
Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning	Oct 13, 2021	Active Learning	—Unverified	0
Real-Time Learning from An Expert in Deep Recommendation Systems with Marginal Distance Probability Distribution	Oct 12, 2021	Active LearningRecommendation Systems	—Unverified	0
AutoNLU: Detecting, root-causing, and fixing NLU model errors	Oct 12, 2021	Active LearningNatural Language Understanding	—Unverified	0
Active Altruism Learning and Information Sufficiency for Autonomous Driving	Oct 9, 2021	Active LearningAutonomous Driving	—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