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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 881–890 of 3073 papers

Title	Date	Tasks	Status	Score
Sample Noise Impact on Active Learning	Sep 3, 2021	Active Learning	CodeCode Available	5
Sampling and Reconstruction of Signals on Product Graphs	Jun 30, 2018	Active LearningRecommendation Systems	CodeCode Available	5
Batch Active Learning at Scale	Jul 29, 2021	Active LearningDiversity	CodeCode Available	5
ScatterShot: Interactive In-context Example Curation for Text Transformation	Feb 14, 2023	Active LearningIn-Context Learning	CodeCode Available	5
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning	Jun 19, 2019	Active Learning	CodeCode Available	5
Selection via Proxy: Efficient Data Selection for Deep Learning	Jun 26, 2019	Active LearningComputational Efficiency	CodeCode Available	5
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing	Dec 27, 2021	Active LearningDiversity	CodeCode Available	5
BAL: Balancing Diversity and Novelty for Active Learning	Dec 26, 2023	Active LearningDiversity	CodeCode Available	5
Active Gradual Machine Learning for Entity Resolution	Jan 16, 2022	Active LearningBIG-bench Machine Learning	CodeCode Available	5
Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle	May 30, 2021	Active Learning	CodeCode Available	5

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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