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 2776–2800 of 3073 papers

Title	Date	Tasks	Status
Noisy Natural Gradient as Variational Inference	Dec 6, 2017	Active LearningEfficient Exploration	CodeCode Available
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models	May 9, 2019	Active LearningBIG-bench Machine Learning	CodeCode Available
Active Learning Using Uncertainty Information	Feb 27, 2017	Active Learning	CodeCode Available
The Sample Complexity of Best-k Items Selection from Pairwise Comparisons	Jul 6, 2020	Active Learning	CodeCode Available
Non-Parametric Calibration for Classification	Jun 12, 2019	Active LearningClassification	CodeCode Available
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
Knowledge-driven Active Learning	Oct 15, 2021	Active LearningMulti-Label Classification	CodeCode Available
ActiveEA: Active Learning for Neural Entity Alignment	Oct 13, 2021	Active LearningEntity Alignment	CodeCode Available
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning	Feb 28, 2024	Active LearningGaussian Processes	CodeCode Available
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets	Jul 19, 2023	Active LearningGraph Learning	CodeCode Available
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted Networks	May 29, 2025	Active Learning	CodeCode Available
Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification	Apr 22, 2022	Active LearningMetric Learning	CodeCode Available
An Atomistic Machine Learning Package for Surface Science and Catalysis	Apr 1, 2019	Active LearningBIG-bench Machine Learning	CodeCode Available
A Bayesian Approach for Sequence Tagging with Crowds	Nov 2, 2018	Active LearningArgument Mining	CodeCode Available
Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training	Sep 29, 2024	Active LearningArtifact Detection	CodeCode Available
Progress & Compress: A scalable framework for continual learning	May 16, 2018	Active LearningAtari Games	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