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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 1351–1360 of 3073 papers

Title	Date	Tasks	Status	Hype
Representative Subset Selection for Efficient Fine-Tuning in Self-Supervised Speech Recognition	Mar 18, 2022	Active LearningAutomatic Speech Recognition	—Unverified	0
Multilingual Detection of Personal Employment Status on Twitter	Mar 17, 2022	Active LearningDiversity	CodeCode Available	0
Nearest Neighbor Classifier with Margin Penalty for Active Learning	Mar 17, 2022	Active Learning	CodeCode Available	0
A Framework and Benchmark for Deep Batch Active Learning for Regression	Mar 17, 2022	Active Learningregression	CodeCode Available	1
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy	Mar 15, 2022	Active LearningClassification	CodeCode Available	1
Uncertainty Estimation for Language Reward Models	Mar 14, 2022	Active LearningReinforcement Learning (RL)	—Unverified	0
Active Learning by Feature Mixing	Mar 14, 2022	Active Learning	CodeCode Available	1
A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluids	Mar 11, 2022	Active Learning	CodeCode Available	0
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering	Mar 11, 2022	Active LearningCollaborative Filtering	—Unverified	0
Learning Distinctive Margin toward Active Domain Adaptation	Mar 11, 2022	Active LearningDomain Adaptation	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