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 181–190 of 3073 papers

Title	Date	Tasks	Status	Hype
Conditioning Sparse Variational Gaussian Processes for Online Decision-making	Oct 28, 2021	Active LearningDecision Making	CodeCode Available	1
Confidence-Aware Learning for Deep Neural Networks	Jul 3, 2020	Active LearningOut-of-Distribution Detection	CodeCode Available	1
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain Knowledge	Jun 1, 2020	Active LearningDomain Adaptation	CodeCode Available	1
Continuous Learning for Android Malware Detection	Feb 8, 2023	Active LearningAndroid Malware Detection	CodeCode Available	1
Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks	Apr 3, 2023	Active LearningQuestion Answering	CodeCode Available	1
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis	Oct 28, 2022	Active LearningAttribute	CodeCode Available	1
Active Learning for Optimal Intervention Design in Causal Models	Sep 10, 2022	Active LearningExperimental Design	CodeCode Available	1
Unsupervised Selective Labeling for More Effective Semi-Supervised Learning	Oct 6, 2021	Active LearningSemi-Supervised Image Classification (Cold Start)	CodeCode Available	1
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression	Nov 16, 2022	Active Learning	CodeCode Available	1
Active Learning Helps Pretrained Models Learn the Intended Task	Apr 18, 2022	Active Learning	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