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 1371–1380 of 3073 papers

Title	Date	Tasks	Status	Hype
Biological Sequence Design with GFlowNets	Mar 2, 2022	Active LearningDiversity	CodeCode Available	1
Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution Equations	Mar 2, 2022	Active LearningVocal Bursts Intensity Prediction	CodeCode Available	1
Information Gain Propagation: a new way to Graph Active Learning with Soft Labels	Mar 2, 2022	Active Learning	CodeCode Available	1
Active learning with binary models for real time data labelling	Feb 28, 2022	Active Learning	—Unverified	0
Bayesian Active Learning for Discrete Latent Variable Models	Feb 27, 2022	Active LearningDecision Making	—Unverified	0
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning	Feb 25, 2022	Active LearningDiversity	—Unverified	0
Modulation and signal class labelling using active learning and classification using machine learning	Feb 23, 2022	Active LearningBIG-bench Machine Learning	—Unverified	0
Parallel MCMC Without Embarrassing Failures	Feb 22, 2022	Active LearningBayesian Inference	CodeCode Available	0
t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning	Feb 21, 2022	Active Learning	—Unverified	0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets	Feb 21, 2022	Active LearningData Augmentation	—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