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 1181–1190 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Learning for Optimal Intervention Design in Causal Models	Sep 10, 2022	Active LearningExperimental Design	CodeCode Available	1
Active Learning of Classifiers with Label and Seed Queries	Sep 8, 2022	Active Learning	—Unverified	0
Peer to Peer Learning Platform Optimized With Machine Learning	Sep 7, 2022	Active Learning	—Unverified	0
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach	Sep 6, 2022	Active LearningDepression Detection	—Unverified	0
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification	Sep 5, 2022	Active LearningDiagnostic	—Unverified	0
Domain Adaptation from Scratch	Sep 2, 2022	Active LearningDomain Adaptation	CodeCode Available	0
Active learning-assisted neutron spectroscopy with log-Gaussian processes	Sep 2, 2022	Active LearningGaussian Processes	—Unverified	0
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation	Sep 1, 2022	Active LearningGPR	CodeCode Available	0
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization	Aug 31, 2022	Action LocalizationActive Learning	—Unverified	0
Confidence Estimation for Object Detection in Document Images	Aug 29, 2022	Active LearningDescriptive	—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