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 2331–2340 of 3073 papers

Title	Date	Tasks	Status	Hype
Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples	Jul 17, 2019	Active LearningExperimental Design	—Unverified	0
Modeling Human Annotation Errors to Design Bias-Aware Systems for Social Stream Processing	Jul 16, 2019	Active LearningBIG-bench Machine Learning	—Unverified	0
MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation	Jul 16, 2019	Active Learningnamed-entity-recognition	—Unverified	0
Discriminative Active Learning	Jul 15, 2019	Active LearningBinary Classification	CodeCode Available	0
Self-Regulated Interactive Sequence-to-Sequence Learning	Jul 11, 2019	Active LearningMachine Translation	CodeCode Available	0
Deep Active Learning for Axon-Myelin Segmentation on Histology Data	Jul 11, 2019	Active LearningAnatomy	CodeCode Available	1
The Power of Comparisons for Actively Learning Linear Classifiers	Jul 8, 2019	Active LearningPAC learning	—Unverified	0
A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images	Jul 3, 2019	Active LearningCollaborative Filtering	—Unverified	0
AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging	Jul 1, 2019	Active Learningnamed-entity-recognition	—Unverified	0
Learning How to Active Learn by Dreaming	Jul 1, 2019	Active Learningnamed-entity-recognition	CodeCode Available	0

Show:10 25 50

← PrevPage 234 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