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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 821–830 of 3073 papers

Title	Date	Tasks	Status	Score
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning	Apr 19, 2024	Active Learning	CodeCode Available	5
A Survey of Deep Active Learning	Aug 30, 2020	Active Learningspeech-recognition	CodeCode Available	5
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture	May 22, 2023	Active LearningDecision Making	CodeCode Available	5
Batch Active Learning at Scale	Jul 29, 2021	Active LearningDiversity	CodeCode Available	5
On Bayesian Search for the Feasible Space Under Computationally Expensive Constraints	Apr 23, 2020	Active LearningGaussian Processes	CodeCode Available	5
Onception: Active Learning with Expert Advice for Real World Machine Translation	Mar 9, 2022	Active LearningMachine Translation	CodeCode Available	5
On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction	Jun 29, 2021	Active LearningGraph Neural Network	CodeCode Available	5
A Survey on Deep Learning of Small Sample in Biomedical Image Analysis	Aug 1, 2019	Active LearningData Augmentation	CodeCode Available	5
A Survey on Multi-Task Learning	Jul 25, 2017	Active LearningClustering	CodeCode Available	5
Batch Active Learning Using Determinantal Point Processes	Jun 19, 2019	Active LearningDiversity	CodeCode Available	5

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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