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 1571–1580 of 3073 papers

Title	Date	Tasks	Status
Limitations of Active Learning With Deep Transformer Language Models	Sep 29, 2021	Active Learning	—Unverified
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach	Sep 29, 2021	Active LearningMulti-Label Learning	—Unverified
Active Learning for Argument Mining: A Practical Approach	Sep 28, 2021	Active LearningArgument Mining	—Unverified
What to Prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development	Sep 28, 2021	Active LearningSentence	—Unverified
Improving Question Answering Performance Using Knowledge Distillation and Active Learning	Sep 26, 2021	Active LearningKnowledge Distillation	CodeCode Available
Data Summarization via Bilevel Optimization	Sep 26, 2021	Active LearningBilevel Optimization	—Unverified
Bayesian Active Learning for Sim-to-Real Robotic Perception	Sep 23, 2021	Active LearningInformativeness	—Unverified
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification	Sep 23, 2021	Active Learning	—Unverified
Active Learning for Argument Strength Estimation	Sep 23, 2021	Active LearningArgument Mining	CodeCode Available
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers	Sep 22, 2021	Active LearningAdversarial Attack	—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