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 526–550 of 3073 papers

Title	Date	Tasks	Status	Score
Cost-Effective Active Learning for Melanoma Segmentation	Nov 24, 2017	Active LearningImage Segmentation	CodeCode Available	5
Deep Active Learning with Adaptive Acquisition	Jun 27, 2019	Active LearningModel Selection	CodeCode Available	5
Confidence Estimation Using Unlabeled Data	Jul 19, 2023	Active Learningimage-classification	CodeCode Available	5
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations	Feb 2, 2024	Active Learning	CodeCode Available	5
Confidence-Aware Active Feedback for Interactive Instance Search	Oct 23, 2021	Active LearningInstance Search	CodeCode Available	5
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning	Mar 18, 2022	Active Learning	CodeCode Available	5
Computational Job Market Analysis with Natural Language Processing	Apr 29, 2024	Active LearningDe-identification	CodeCode Available	5
Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression	Jul 31, 2023	Active LearningDiversity	CodeCode Available	5
Computational Assessment of Hyperpartisanship in News Titles	Jan 16, 2023	Active LearningLanguage Modelling	CodeCode Available	5
Compute-Efficient Active Learning	Jan 15, 2024	Active Learning	CodeCode Available	5
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation	Nov 6, 2024	Active LearningBayesian Optimization	CodeCode Available	5
Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels	Dec 2, 2019	Active Learning	CodeCode Available	5
Active Learning-Based Species Range Estimation	Nov 3, 2023	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 in CNNs via Expected Improvement Maximization	Nov 27, 2020	Active Learning	CodeCode Available	5
Active Collaborative Sensing for Energy Breakdown	Sep 2, 2019	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 learning in annotating micro-blogs dealing with e-reputation	Jun 16, 2017	Active LearningInformation Retrieval	CodeCode Available	5
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees	Feb 3, 2024	Active Learning	CodeCode Available	5
Constraining the Parameters of High-Dimensional Models with Active Learning	May 19, 2019	Active LearningAstronomy	CodeCode Available	5
Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias	May 27, 2022	Active LearningExperimental Design	CodeCode Available	5
CFlowNets: Continuous Control with Generative Flow Networks	Mar 4, 2023	Active Learningcontinuous-control	CodeCode Available	5
Class Balance Matters to Active Class-Incremental Learning	Dec 9, 2024	Active Learningclass-incremental learning	CodeCode Available	5
Active Learning from Positive and Unlabeled Data	Feb 24, 2016	Active LearningInformativeness	CodeCode Available	5
CAMAL: Optimizing LSM-trees via Active Learning	Sep 23, 2024	Active Learning	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