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 271–280 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Invariant Causal Prediction: Experiment Selection through Stability	Jun 10, 2020	Active LearningPrediction	CodeCode Available	1
Active Learning for Optimal Intervention Design in Causal Models	Sep 10, 2022	Active LearningExperimental Design	CodeCode Available	1
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset	Jun 19, 2024	Active LearningDomain Adaptation	CodeCode Available	1
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks	Jan 27, 2021	Active LearningUncertainty Quantification	CodeCode Available	1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages	Nov 7, 2022	Active LearningLanguage Modeling	CodeCode Available	1
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts	Feb 3, 2018	Active LearningTransfer Learning	CodeCode Available	1
AISecKG: Knowledge Graph Dataset for Cybersecurity Education	Mar 30, 2023	Active LearningKnowledge Graphs	CodeCode Available	1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation	Feb 14, 2022	Active LearningDomain Adaptation	CodeCode Available	1
Active Learning for Open-set Annotation	Jan 18, 2022	Active Learning	CodeCode Available	1
A Simple Baseline for Low-Budget Active Learning	Oct 22, 2021	Active Learningimage-classification	CodeCode Available	1

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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