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 2781–2790 of 3073 papers

Title	Date	Tasks	Status
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation	Sep 1, 2022	Active LearningGPR	CodeCode Available
Efficient Classification with Counterfactual Reasoning and Active Learning	Jul 25, 2022	Active LearningClassification	CodeCode Available
Key Patch Proposer: Key Patches Contain Rich Information	Feb 18, 2024	Active LearningSemantic Segmentation	CodeCode Available
Efficient Concept Drift Handling for Batch Android Malware Detection Models	Sep 18, 2023	Active LearningAndroid Malware Detection	CodeCode Available
Nonstationary data stream classification with online active learning and siamese neural networks	Oct 3, 2022	Active Learning	CodeCode Available
Non-Uniform Subset Selection for Active Learning in Structured Data	Jun 1, 2017	Active LearningActivity Recognition	CodeCode Available
An Information-Theoretic Framework for Unifying Active Learning Problems	Dec 19, 2020	Active LearningBayesian Optimization	CodeCode Available
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling	Feb 2, 2023	Active Learning	CodeCode Available
An Information Retrieval Approach to Building Datasets for Hate Speech Detection	Jun 17, 2021	Active LearningExplanation Generation	CodeCode Available
Efficient Human-in-the-loop System for Guiding DNNs Attention	Jun 13, 2022	Active Learningimage-classification	CodeCode Available

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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