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 2771–2780 of 3073 papers

Title	Date	Tasks	Status
Active Learning for Decision-Making from Imbalanced Observational Data	Apr 10, 2019	Active LearningDecision Making	CodeCode Available
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs	Mar 29, 2023	Active LearningDecision Making	CodeCode Available
Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space	Aug 25, 2017	Active Learning	CodeCode Available
STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings	May 18, 2023	Active LearningAutonomous Vehicles	CodeCode Available
The Right Tool for the Job: Matching Active Learning Techniques to Learning Objectives	May 6, 2022	Active Learning	CodeCode Available
Noisy Natural Gradient as Variational Inference	Dec 6, 2017	Active LearningEfficient Exploration	CodeCode Available
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models	May 9, 2019	Active LearningBIG-bench Machine Learning	CodeCode Available
Active Learning Using Uncertainty Information	Feb 27, 2017	Active Learning	CodeCode Available
The Sample Complexity of Best-k Items Selection from Pairwise Comparisons	Jul 6, 2020	Active Learning	CodeCode Available
Non-Parametric Calibration for Classification	Jun 12, 2019	Active LearningClassification	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