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 1581–1590 of 3073 papers

Title	Date	Tasks	Status
The Impact of Typicality for Informative Representative Selection	Jul 1, 2017	Active LearningData Compression	—Unverified
The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning	Feb 27, 2019	Active LearningMetric Learning	—Unverified
The Infinite Index: Information Retrieval on Generative Text-To-Image Models	Dec 14, 2022	Active LearningGame Design	—Unverified
The Power of Comparisons for Actively Learning Linear Classifiers	Jul 8, 2019	Active LearningPAC learning	—Unverified
The Power of Ensembles for Active Learning in Image Classification	Jun 1, 2018	Active LearningGeneral Classification	—Unverified
The Power of Localization for Efficiently Learning Linear Separators with Noise	Jul 31, 2013	Active Learning	—Unverified
The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation	Jun 28, 2019	Active Learning	—Unverified
The Relationship Between Agnostic Selective Classification Active Learning and the Disagreement Coefficient	Mar 19, 2017	Active LearningGeneral Classification	—Unverified
The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning	Nov 29, 2018	Active LearningDeep Learning	—Unverified
The Role of Higher-Order Cognitive Models in Active Learning	Jan 9, 2024	Active 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