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 501–525 of 3073 papers

Title	Date	Tasks	Status	Score
Cost-Effective Active Learning for Melanoma Segmentation	Nov 24, 2017	Active LearningImage Segmentation	CodeCode Available	5
Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter	Jun 15, 2023	Active Learning	CodeCode Available	5
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation	Nov 6, 2024	Active LearningBayesian Optimization	CodeCode Available	5
Constraining the Parameters of High-Dimensional Models with Active Learning	May 19, 2019	Active LearningAstronomy	CodeCode Available	5
Confidence Estimation Using Unlabeled Data	Jul 19, 2023	Active Learningimage-classification	CodeCode Available	5
Gradient and Uncertainty Enhanced Sequential Sampling for Global Fit	Sep 29, 2023	Active LearningExperimental Design	CodeCode Available	5
Context Selection and Rewriting for Video-based Educational Question Generation	Apr 28, 2025	Active LearningQuestion Generation	CodeCode Available	5
Graph-boosted Active Learning for Multi-Source Entity Resolution	Sep 30, 2021	Active LearningEntity Resolution	CodeCode Available	5
Graudally Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound Images	Aug 19, 2020	Active LearningObject	CodeCode Available	5
Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation	Nov 28, 2019	Active LearningBayesian Optimisation	CodeCode Available	5
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations	Feb 2, 2024	Active Learning	CodeCode Available	5
Compute-Efficient Active Learning	Jan 15, 2024	Active Learning	CodeCode Available	5
Computational Assessment of Hyperpartisanship in News Titles	Jan 16, 2023	Active LearningLanguage Modelling	CodeCode Available	5
Computational Job Market Analysis with Natural Language Processing	Apr 29, 2024	Active LearningDe-identification	CodeCode Available	5
Confidence-Aware Active Feedback for Interactive Instance Search	Oct 23, 2021	Active LearningInstance Search	CodeCode Available	5
Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels	Dec 2, 2019	Active Learning	CodeCode Available	5
Committee neural network potentials control generalization errors and enable active learning	Jun 2, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	5
Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic Actuators	Apr 1, 2025	Active Learning	CodeCode Available	5
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory Exploration	Sep 28, 2023	Active LearningGaussian Processes	CodeCode Available	5
Active Classification with Uncertainty Comparison Queries	Aug 3, 2020	Active LearningClassification	CodeCode Available	5
Clinical Trial Active Learning	Jul 20, 2023	Active Learning	CodeCode Available	5
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees	Feb 3, 2024	Active Learning	CodeCode Available	5
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics	Nov 25, 2021	Active LearningAge Estimation	CodeCode Available	5
CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction	Apr 8, 2022	Active LearningArticles	CodeCode Available	5
Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias	May 27, 2022	Active LearningExperimental Design	CodeCode Available	5

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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