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 2851–2860 of 3073 papers

Title	Date	Tasks	Status
Active Learning for Delineation of Curvilinear Structures	Dec 2, 2015	Active LearningGeneral Classification	—Unverified
Multi-Class Multi-Annotator Active Learning With Robust Gaussian Process for Visual Recognition	Dec 1, 2015	Active LearningBayesian Inference	—Unverified
Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation	Dec 1, 2015	Active LearningSegmentation	—Unverified
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning	Dec 1, 2015	Active LearningBIG-bench Machine Learning	—Unverified
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching	Dec 1, 2015	Active Learning	—Unverified
Convergence rates of sub-sampled Newton methods	Dec 1, 2015	Active LearningBinary Classification	—Unverified
Context Aware Active Learning of Activity Recognition Models	Dec 1, 2015	Active LearningActivity Recognition	—Unverified
Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond	Nov 21, 2015	Active LearningBayesian Optimization	—Unverified
Near-Optimal Active Learning of Multi-Output Gaussian Processes	Nov 21, 2015	Active LearningGaussian Processes	CodeCode Available
The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition	Nov 20, 2015	Active LearningFine-Grained Image Classification	CodeCode Available

Show:10 25 50

← PrevPage 286 of 308Next →

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