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 1351–1360 of 3073 papers

Title	Date	Tasks	Status
Fair Robust Active Learning by Joint Inconsistency	Sep 22, 2022	Active LearningAdversarial Attack	—Unverified
A Bibliographic View on Constrained Clustering	Sep 22, 2022	Active LearningClustering	CodeCode Available
Active Keyword Selection to Track Evolving Topics on Twitter	Sep 22, 2022	Active Learning	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
FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering	Sep 21, 2022	Active LearningClustering	CodeCode Available
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation	Sep 20, 2022	Active LearningUncertainty Quantification	—Unverified
Predictive Scale-Bridging Simulations through Active Learning	Sep 20, 2022	Active Learning	—Unverified
Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography	Sep 20, 2022	Active LearningCPU	—Unverified
Introspective Learning : A Two-Stage Approach for Inference in Neural Networks	Sep 17, 2022	Active LearningDecision Making	CodeCode Available
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning	Sep 16, 2022	Active LearningArticles	—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