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 721–730 of 3073 papers

Title	Date	Tasks	Status	Hype
Explaining Predictive Uncertainty with Information Theoretic Shapley Values	Sep 21, 2023	Active LearningDecision Making Under Uncertainty	CodeCode Available	1
Evidential uncertainties on rich labels for active learning	Sep 21, 2023	Active Learning	—Unverified	0
Active Learning for Multilingual Fingerspelling Corpora	Sep 21, 2023	Active Learning	—Unverified	0
Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening	Sep 20, 2023	Active LearningBayesian Optimization	—Unverified	0
Active anomaly detection based on deep one-class classification	Sep 18, 2023	Active LearningAnomaly Detection	—Unverified	0
Causal Discovery and Prediction: Methods and Algorithms	Sep 18, 2023	Active LearningCausal Discovery	—Unverified	0
Efficient Concept Drift Handling for Batch Android Malware Detection Models	Sep 18, 2023	Active LearningAndroid Malware Detection	CodeCode Available	0
Active Learning for Semantic Segmentation with Multi-class Label Query	Sep 17, 2023	Active LearningMultiple Instance Learning	CodeCode Available	0
Enhancing personalised thermal comfort models with Active Learning for improved HVAC controls	Sep 16, 2023	Active Learning	—Unverified	0
Interactively Teaching an Inverse Reinforcement Learner with Limited Feedback	Sep 16, 2023	Active LearningDecision Making	CodeCode Available	0

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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