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 2231–2240 of 3073 papers

Title	Date	Tasks	Status
Interactive Robot Training for Non-Markov Tasks	Mar 4, 2020	Active Learning	—Unverified
Interactive Structure Learning with Structural Query-by-Committee	Dec 1, 2018	Active Learning	—Unverified
Learning to Optimize Contextually Constrained Problems for Real-Time Decision-Generation	May 23, 2018	Active LearningPortfolio Optimization	—Unverified
Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation	Apr 13, 2021	Active LearningGeneral Classification	—Unverified
Interpret-able feedback for AutoML systems	Feb 22, 2021	Active LearningAutoML	—Unverified
Analyzing Data Selection Techniques with Tools from the Theory of Information Losses	Feb 25, 2019	Active LearningExperimental Design	—Unverified
Introducing Geometry in Active Learning for Image Segmentation	Aug 20, 2015	Active LearningImage Segmentation	—Unverified
Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil	Aug 19, 2021	Active LearningTransfer Learning	—Unverified
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling	Mar 1, 2018	Active Learningreinforcement-learning	—Unverified
Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasure	Mar 28, 2022	Active LearningData Augmentation	—Unverified

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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