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 2931–2940 of 3073 papers

Title	Date	Tasks	Status
On the Relationship between Data Efficiency and Error for Uncertainty Sampling	Jun 15, 2018	Active Learningregression	CodeCode Available
Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition	May 1, 2024	Active LearningEmotion Recognition	CodeCode Available
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning	Nov 19, 2018	Active LearningQ-Learning	CodeCode Available
Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models	Mar 2, 2024	Active LearningComputational Efficiency	CodeCode Available
Active Learning for Manifold Gaussian Process Regression	Jun 26, 2025	Active LearningDimensionality Reduction	CodeCode Available
Federated Active Learning for Target Domain Generalisation	Dec 4, 2023	Active LearningFederated Learning	CodeCode Available
Active Learning in CNNs via Expected Improvement Maximization	Nov 27, 2020	Active Learning	CodeCode Available
Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss	Dec 5, 2018	Active Learning	CodeCode Available
Feedback Coding for Active Learning	Feb 28, 2021	Active Learning	CodeCode Available
Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation	Nov 1, 2019	Active LearningMachine Translation	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