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 2041–2050 of 3073 papers

Title	Date	Tasks	Status
Active Learning of Causal Structures with Deep Reinforcement Learning	Sep 7, 2020	Active LearningDeep Reinforcement Learning	—Unverified
Active deep learning method for the discovery of objects of interest in large spectroscopic surveys	Sep 7, 2020	Active Learning	—Unverified
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation	Sep 6, 2020	Active LearningFeature Importance	—Unverified
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning	Sep 3, 2020	Active LearningContinual Learning	—Unverified
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning	Sep 3, 2020	Active Learning	—Unverified
ALEX: Active Learning based Enhancement of a Model's Explainability	Sep 2, 2020	Active Learning	—Unverified
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification	Sep 1, 2020	Active LearningClassification	—Unverified
Wireless for Machine Learning	Aug 31, 2020	Active LearningBIG-bench Machine Learning	—Unverified
A Survey of Deep Active Learning	Aug 30, 2020	Active Learningspeech-recognition	CodeCode Available
Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition	Aug 28, 2020	Active LearningInformativeness	—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