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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 1771–1780 of 3073 papers

Title	Date	Tasks	Status	Hype
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes	Apr 8, 2021	Active Learning	—Unverified	0
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations	Apr 8, 2021	Active Learning	—Unverified	0
Deep Indexed Active Learning for Matching Heterogeneous Entity Representations	Apr 8, 2021	Active LearningBlocking	CodeCode Available	1
Active learning using weakly supervised signals for quality inspection	Apr 7, 2021	Active Learning	—Unverified	0
Low-Regret Active learning	Apr 6, 2021	Active LearningInformativeness	—Unverified	0
Multiple instance active learning for object detection	Apr 6, 2021	Active LearningActive Object Detection	CodeCode Available	1
Fast Design Space Exploration of Nonlinear Systems: Part II	Apr 5, 2021	Active Learning	—Unverified	0
Optimal Sampling Gaps for Adaptive Submodular Maximization	Apr 5, 2021	Active LearningBIG-bench Machine Learning	—Unverified	0
Automated Performance Testing Based on Active Deep Learning	Apr 5, 2021	Active LearningDeep Learning	CodeCode Available	0
Fast Design Space Exploration of Nonlinear Systems: Part I	Apr 5, 2021	Active LearningBayesian Optimization	—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