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 2921–2930 of 3073 papers

Title	Date	Tasks	Status
Can I see an Example? Active Learning the Long Tail of Attributes and Relations	Mar 11, 2022	Active Learning	—Unverified
Can Natural Language Processing Become Natural Language Coaching?	Jul 1, 2015	Active LearningGrammatical Error Detection	—Unverified
Can Strategic Data Collection Improve the Performance of Poverty Prediction Models?	Nov 16, 2022	Active LearningHumanitarian	—Unverified
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions	Nov 21, 2022	Active LearningTransfer Learning	—Unverified
Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions	Sep 14, 2017	Active Learning	—Unverified
Causal Discovery and Prediction: Methods and Algorithms	Sep 18, 2023	Active LearningCausal Discovery	—Unverified
CELEST: Federated Learning for Globally Coordinated Threat Detection	May 23, 2022	Active LearningFederated Learning	—Unverified
CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation	Oct 23, 2018	Active LearningImage Segmentation	—Unverified
Certifying One-Phase Technology-Assisted Reviews	Aug 29, 2021	Active LearningTAR	—Unverified
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images	Jan 26, 2022	Active LearningBIG-bench Machine Learning	—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