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 1161–1170 of 3073 papers

Title	Date	Tasks	Status
Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data	Sep 28, 2022	Active LearningFew-Shot Learning	—Unverified
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings	Sep 23, 2022	Active Learning	—Unverified
From Weakly Supervised Learning to Active Learning	Sep 23, 2022	Active LearningWeakly-supervised Learning	—Unverified
Smart Active Sampling to enhance Quality Assurance Efficiency	Sep 23, 2022	Active Learning	—Unverified
A Bibliographic View on Constrained Clustering	Sep 22, 2022	Active LearningClustering	CodeCode Available
Fair Robust Active Learning by Joint Inconsistency	Sep 22, 2022	Active LearningAdversarial Attack	—Unverified
Active Keyword Selection to Track Evolving Topics on Twitter	Sep 22, 2022	Active Learning	CodeCode Available
FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering	Sep 21, 2022	Active LearningClustering	CodeCode Available
Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning	Sep 21, 2022	Abusive LanguageActive Learning	CodeCode Available
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation	Sep 20, 2022	Active LearningUncertainty Quantification	—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