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 1721–1730 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Learning of Continuous-time Bayesian Networks through Interventions	May 31, 2021	Active LearningExperimental Design	—Unverified	0
Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle	May 30, 2021	Active Learning	CodeCode Available	0
Optimal Sampling Density for Nonparametric Regression	May 25, 2021	Active Learningregression	—Unverified	0
Zero Initialised Unsupervised Active Learning by Optimally Balanced Entropy-Based Sampling for Imbalanced Problems	May 24, 2021	Active LearningBIG-bench Machine Learning	CodeCode Available	0
Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference	May 24, 2021	Active LearningGPR	—Unverified	0
Cost-Accuracy Aware Adaptive Labeling for Active Learning	May 24, 2021	Active LearningInformativeness	CodeCode Available	0
Mapping oil palm density at country scale: An active learning approach	May 24, 2021	Active LearningDensity Estimation	—Unverified	0
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains	May 23, 2021	Active LearningBayesian Optimisation	CodeCode Available	1
LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning	May 21, 2021	Active LearningData Augmentation	CodeCode Available	1
Coresets for Classification – Simplified and Strengthened	May 21, 2021	Active LearningClassification	—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