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 641–650 of 3073 papers

Title	Date	Tasks	Status	Score
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture	May 22, 2023	Active LearningDecision Making	CodeCode Available	5
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?	Nov 6, 2020	Active LearningBenchmarking	CodeCode Available	5
Building a comprehensive syntactic and semantic corpus of Chinese clinical texts	Nov 7, 2016	Active LearningPOS	CodeCode Available	5
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation	Dec 1, 2018	Active LearningBayesian Optimisation	CodeCode Available	5
Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising Trees	Oct 27, 2021	Active Learning	CodeCode Available	5
Deep Active Learning with Augmentation-based Consistency Estimation	Nov 5, 2020	Active LearningData Augmentation	CodeCode Available	5
Bayesian Neural Scaling Laws Extrapolation with Prior-Fitted Networks	May 29, 2025	Active Learning	CodeCode Available	5
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions	Oct 5, 2022	Active LearningAutonomous Driving	CodeCode Available	5
Deep Bayesian Active Learning for Preference Modeling in Large Language Models	Jun 14, 2024	Active Learning	CodeCode Available	5
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning	Apr 19, 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