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 426–450 of 3073 papers

Title	Date	Tasks	Status	Score
Cooperative Inverse Reinforcement Learning	Jun 9, 2016	Active Learningreinforcement-learning	CodeCode Available	5
Active Learning of Spin Network Models	Mar 25, 2019	Active Learning	CodeCode Available	5
Active Learning for Decision-Making from Imbalanced Observational Data	Apr 10, 2019	Active LearningDecision Making	CodeCode Available	5
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics	Nov 25, 2021	Active LearningAge Estimation	CodeCode Available	5
Active Learning with Contrastive Pre-training for Facial Expression Recognition	Jul 6, 2023	Active LearningFacial Expression Recognition	CodeCode Available	5
Environmental Sensor Placement with Convolutional Gaussian Neural Processes	Nov 18, 2022	Active LearningGaussian Processes	CodeCode Available	5
Active Learning for Deep Gaussian Process Surrogates	Dec 15, 2020	Active LearningGaussian Processes	CodeCode Available	5
Constraining the Parameters of High-Dimensional Models with Active Learning	May 19, 2019	Active LearningAstronomy	CodeCode Available	5
Detecting Anatomical and Functional Connectivity Relations in Biomedical Literature via Language Representation Models	Jun 1, 2021	Active LearningFunctional Connectivity	CodeCode Available	5
A3: Active Adversarial Alignment for Source-Free Domain Adaptation	Sep 27, 2024	Active LearningDomain Adaptation	CodeCode Available	5
Active Learning with Gaussian Processes for High Throughput Phenotyping	Jan 21, 2019	Active LearningGaussian Processes	CodeCode Available	5
Context Selection and Rewriting for Video-based Educational Question Generation	Apr 28, 2025	Active LearningQuestion Generation	CodeCode Available	5
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context Adaptation	Jan 14, 2025	Active Learningclass-incremental learning	CodeCode Available	5
Correlation Clustering with Adaptive Similarity Queries	May 28, 2019	Active LearningClustering	CodeCode Available	5
Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning Approach	Jul 18, 2018	Active LearningBIG-bench Machine Learning	CodeCode Available	5
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models	Jun 22, 2021	Active LearningNumerical Integration	CodeCode Available	5
Active DOP: A constituency treebank annotation tool with online learning	Aug 1, 2018	Active LearningFeature Engineering	CodeCode Available	5
Exploring Data Redundancy in Real-world Image Classification through Data Selection	Jun 25, 2023	Active LearningContinual Learning	CodeCode Available	5
Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation	Jan 27, 2020	Active LearningBayesian Optimization	CodeCode Available	5
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations	Feb 2, 2024	Active Learning	CodeCode Available	5
Active Learning with Partial Feedback	Feb 21, 2018	Active Learning	CodeCode Available	5
Compute-Efficient Active Learning	Jan 15, 2024	Active Learning	CodeCode Available	5
Computational Assessment of Hyperpartisanship in News Titles	Jan 16, 2023	Active LearningLanguage Modelling	CodeCode Available	5
Active Learning for Argument Strength Estimation	Sep 23, 2021	Active LearningArgument Mining	CodeCode Available	5
Computational Job Market Analysis with Natural Language Processing	Apr 29, 2024	Active LearningDe-identification	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