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 511–520 of 3073 papers

Title	Date	Tasks	Status	Score
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations	Feb 2, 2024	Active Learning	CodeCode Available	5
Confidence Estimation Using Unlabeled Data	Jul 19, 2023	Active Learningimage-classification	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
Committee neural network potentials control generalization errors and enable active learning	Jun 2, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	5
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees	Feb 3, 2024	Active Learning	CodeCode Available	5
Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels	Dec 2, 2019	Active Learning	CodeCode Available	5
Computational Assessment of Hyperpartisanship in News Titles	Jan 16, 2023	Active LearningLanguage Modelling	CodeCode Available	5
Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic Actuators	Apr 1, 2025	Active Learning	CodeCode Available	5
Clinical Trial Active Learning	Jul 20, 2023	Active Learning	CodeCode Available	5
Class Balance Matters to Active Class-Incremental Learning	Dec 9, 2024	Active Learningclass-incremental 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