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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 231–240 of 3073 papers

Title	Date	Tasks	Status	Hype
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning	Apr 16, 2021	Active LearningNatural Language Understanding	CodeCode Available	1
Can Active Learning Preemptively Mitigate Fairness Issues?	Apr 14, 2021	Active LearningFairness	CodeCode Available	1
All you need are a few pixels: semantic segmentation with PixelPick	Apr 13, 2021	Active LearningAll	CodeCode Available	1
Model Learning with Personalized Interpretability Estimation (ML-PIE)	Apr 13, 2021	Active Learning	CodeCode Available	1
Deep Indexed Active Learning for Matching Heterogeneous Entity Representations	Apr 8, 2021	Active LearningBlocking	CodeCode Available	1
Multiple instance active learning for object detection	Apr 6, 2021	Active LearningActive Object Detection	CodeCode Available	1
Is segmentation uncertainty useful?	Mar 30, 2021	Active LearningImage Segmentation	CodeCode Available	1
Active Learning for Deep Object Detection via Probabilistic Modeling	Mar 30, 2021	Active LearningClassification	CodeCode Available	1
Consistency-based Active Learning for Object Detection	Mar 18, 2021	Active LearningClassification	CodeCode Available	1
Active Testing: Sample-Efficient Model Evaluation	Mar 9, 2021	Active LearningGaussian Processes	CodeCode Available	1

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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