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 451–475 of 3073 papers

Title	Date	Tasks	Status	Score
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes	Jul 13, 2024	Active LearningGaussian Processes	CodeCode Available	5
Active^2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation	Nov 1, 2019	Active LearningMachine Translation	CodeCode Available	5
ActiveEA: Active Learning for Neural Entity Alignment	Oct 13, 2021	Active LearningEntity Alignment	CodeCode Available	5
DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation	Jan 22, 2021	Active LearningEnsemble Learning	CodeCode Available	5
An Adversarial Objective for Scalable Exploration	Mar 13, 2020	Active LearningPrediction	CodeCode Available	5
Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition	May 1, 2024	Active LearningEmotion Recognition	CodeCode Available	5
Efficacy of Bayesian Neural Networks in Active Learning	Apr 2, 2021	Active Learning	CodeCode Available	5
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation	Sep 1, 2022	Active LearningGPR	CodeCode Available	5
Confidence Estimation Using Unlabeled Data	Jul 19, 2023	Active Learningimage-classification	CodeCode Available	5
Active Learning for Argument Strength Estimation	Sep 23, 2021	Active LearningArgument Mining	CodeCode Available	5
Context Selection and Rewriting for Video-based Educational Question Generation	Apr 28, 2025	Active LearningQuestion Generation	CodeCode Available	5
Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space	Aug 25, 2017	Active Learning	CodeCode Available	5
Active Learning of Molecular Data for Task-Specific Objectives	Aug 20, 2024	Active LearningGaussian Processes	CodeCode Available	5
Confidence-Aware Active Feedback for Interactive Instance Search	Oct 23, 2021	Active LearningInstance Search	CodeCode Available	5
Actively Discovering New Slots for Task-oriented Conversation	May 6, 2023	Active LearningConversational Search	CodeCode Available	5
Continual Developmental Neurosimulation Using Embodied Computational Agents	Mar 7, 2021	Active LearningContinual Learning	CodeCode Available	5
Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space	Nov 18, 2024	Active Learning	CodeCode Available	5
Enhancing Retinal Disease Classification from OCTA Images via Active Learning Techniques	Jul 21, 2024	Active Learning	CodeCode Available	5
Actively Learning Costly Reward Functions for Reinforcement Learning	Nov 23, 2022	Active LearningDeep Reinforcement Learning	CodeCode Available	5
Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection	Sep 18, 2024	Active Learning	CodeCode Available	5
Actively Learning Gaussian Process Dynamics	Nov 22, 2019	Active Learningregression	CodeCode Available	5
Compute-Efficient Active Learning	Jan 15, 2024	Active Learning	CodeCode Available	5
A Comparison of Strategies for Source-Free Domain Adaptation	May 1, 2022	Active LearningData Augmentation	CodeCode Available	5
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data	Nov 15, 2022	Active LearningDiversity	CodeCode Available	5
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations	Feb 2, 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