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 1381–1390 of 3073 papers

Title	Date	Tasks	Status	Hype
Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks	Feb 19, 2022	Active LearningPrediction	—Unverified	0
Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs	Feb 17, 2022	Active LearningPosition	—Unverified	0
FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction	Feb 16, 2022	Active LearningKnowledge Distillation	CodeCode Available	1
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation	Feb 14, 2022	Active Learning	CodeCode Available	1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation	Feb 14, 2022	Active LearningDomain Adaptation	CodeCode Available	1
Double-Barreled Question Detection at Momentive	Feb 12, 2022	Active LearningBIG-bench Machine Learning	—Unverified	0
Fast Rates in Pool-Based Batch Active Learning	Feb 11, 2022	Active LearningDiversity	—Unverified	0
Improving performance of aircraft detection in satellite imagery while limiting the labelling effort: Hybrid active learning	Feb 10, 2022	Active LearningDiversity	—Unverified	0
Active Learning Improves Performance on Symbolic RegressionTasks in StackGP	Feb 9, 2022	Active Learningregression	—Unverified	0
Sampling Strategy for Fine-Tuning Segmentation Models to Crisis Area under Scarcity of Data	Feb 9, 2022	Active LearningHumanitarian	—Unverified	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