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 1961–1970 of 3073 papers

Title	Date	Tasks	Status	Hype
Uncertainty Based Active Learning Strategy for Interactive Weakly Supervised Learning through Data Programming	Oct 15, 2020	Active LearningBIG-bench Machine Learning	—Unverified	0
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks	Oct 15, 2020	Active LearningOpen-Domain Question Answering	—Unverified	0
Identifying Wrongly Predicted Samples: A Method for Active Learning	Oct 14, 2020	Active Learning	—Unverified	0
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients	Oct 12, 2020	Active LearningAstronomy	CodeCode Available	0
Meta-Active Learning for Node Response Prediction in Graphs	Oct 12, 2020	Active LearningMeta-Learning	—Unverified	0
Pre-trained Language Model Based Active Learning for Sentence Matching	Oct 12, 2020	Active LearningLanguage Modeling	—Unverified	0
Zero-shot Active Learning with Topological Clustering for Multiclass Classification	Oct 10, 2020	Active LearningClustering	—Unverified	0
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks	Oct 10, 2020	Active LearningOpen-Domain Question Answering	CodeCode Available	0
Deep Active Learning for Joint Classification & Segmentation with Weak Annotator	Oct 10, 2020	Active LearningClassification	CodeCode Available	1
Model Exploration with Cost-Aware Learning	Oct 9, 2020	Active LearningBIG-bench Machine Learning	—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