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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 1791–1800 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Structure Learning of Bayesian Networks in an Observational Setting	Mar 25, 2021	Active Learning	CodeCode Available	0
Data driven semi-supervised learning	Mar 18, 2021	Active Learning	—Unverified	0
Consistency-based Active Learning for Object Detection	Mar 18, 2021	Active LearningClassification	CodeCode Available	1
Learning Novel Objects Continually Through Curiosity	Mar 13, 2021	Active LearningContinual Learning	—Unverified	0
Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation	Mar 11, 2021	Active LearningMachine Translation	—Unverified	0
Active Testing: Sample-Efficient Model Evaluation	Mar 9, 2021	Active LearningGaussian Processes	CodeCode Available	1
Continual Developmental Neurosimulation Using Embodied Computational Agents	Mar 7, 2021	Active LearningContinual Learning	CodeCode Available	0
Discrepancy-Based Active Learning for Domain Adaptation	Mar 5, 2021	Active LearningDomain Adaptation	CodeCode Available	1
Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data	Mar 2, 2021	Active LearningMulti-Task Learning	—Unverified	0
Feedback Coding for Active Learning	Feb 28, 2021	Active Learning	CodeCode Available	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