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 1401–1410 of 3073 papers

Title	Date	Tasks	Status
Efficient Classification with Counterfactual Reasoning and Active Learning	Jul 25, 2022	Active LearningClassification	CodeCode Available
Stream-based active learning with linear models	Jul 20, 2022	Active LearningDecision Making	—Unverified
Sample Efficient Learning of Predictors that Complement Humans	Jul 19, 2022	Active Learning	CodeCode Available
Distributed Safe Learning and Planning for Multi-robot Systems	Jul 16, 2022	Active LearningCollision Avoidance	—Unverified
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias	Jul 15, 2022	Active LearningFairness	—Unverified
Plex: Towards Reliability using Pretrained Large Model Extensions	Jul 15, 2022	Active LearningDecision Making	—Unverified
Uncertainty quantification for predictions of atomistic neural networks	Jul 14, 2022	Active LearningProperty Prediction	CodeCode Available
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method	Jul 8, 2022	Active LearningBayesian Optimization	—Unverified
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems	Jul 8, 2022	Active LearningAnomaly Detection	—Unverified
Active Learning and Multi-label Classification for Ellipsis and Coreference Detection in Conversational Question-Answering	Jul 7, 2022	Active LearningConversational Question Answering	—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