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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 2381–2390 of 3073 papers

Title	Date	Tasks	Status
Learning by Active Nonlinear Diffusion	May 30, 2019	Active Learning	—Unverified
The Label Complexity of Active Learning from Observational Data	May 29, 2019	Active Learningcounterfactual	CodeCode Available
Training Data Subset Search with Ensemble Active Learning	May 29, 2019	Active LearningAutonomous Driving	—Unverified
MaxiMin Active Learning in Overparameterized Model Classes	May 29, 2019	Active LearningBIG-bench Machine Learning	—Unverified
Correlation Clustering with Adaptive Similarity Queries	May 28, 2019	Active LearningClustering	CodeCode Available
Dual Active Sampling on Batch-Incremental Active Learning	May 22, 2019	Active Learningimage-classification	CodeCode Available
A framework for the extraction of Deep Neural Networks by leveraging public data	May 22, 2019	Active LearningBIG-bench Machine Learning	—Unverified
Constraining the Parameters of High-Dimensional Models with Active Learning	May 19, 2019	Active LearningAstronomy	CodeCode Available
Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning	May 17, 2019	Active Learning	CodeCode Available
Graph-based Semi-Supervised & Active Learning for Edge Flows	May 17, 2019	Active Learning	CodeCode Available

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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