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 2261–2270 of 3073 papers

Title	Date	Tasks	Status	Hype
Prediction stability as a criterion in active learning	Oct 27, 2019	Active LearningPrediction	—Unverified	0
Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty	Oct 26, 2019	Active LearningExperimental Design	—Unverified	0
Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide	Oct 22, 2019	Active LearningBayesian Optimization	CodeCode Available	0
A deep active learning system for species identification and counting in camera trap images	Oct 22, 2019	Active LearningDecision Making	CodeCode Available	1
Detecting Underspecification with Local Ensembles	Oct 21, 2019	Active LearningOut-of-Distribution Detection	CodeCode Available	1
Mining GOLD Samples for Conditional GANs	Oct 21, 2019	Active Learning	CodeCode Available	0
Gaussian Process Meta-Representations For Hierarchical Neural Network Weight Priors	Oct 16, 2019	Active LearningBayesian Inference	—Unverified	0
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost	Oct 16, 2019	Active Learningimage-classification	—Unverified	0
Active Learning for Graph Neural Networks via Node Feature Propagation	Oct 16, 2019	Active LearningClustering	—Unverified	0
Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning	Oct 11, 2019	Active LearningAll	—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