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 2011–2020 of 3073 papers

Title	Date	Tasks	Status	Hype
Graudally Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound Images	Aug 19, 2020	Active LearningObject	CodeCode Available	0
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning	Aug 18, 2020	Active LearningBIG-bench Machine Learning	—Unverified	0
A Survey of Active Learning for Text Classification using Deep Neural Networks	Aug 17, 2020	Active LearningClassification	—Unverified	0
A New Perspective on Pool-Based Active Classification and False-Discovery Control	Aug 14, 2020	Active LearningBinary Classification	—Unverified	0
Contextual Diversity for Active Learning	Aug 13, 2020	Active LearningDiversity	CodeCode Available	1
Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks	Aug 13, 2020	Active LearningModel Optimization	CodeCode Available	0
Online Graph Completion: Multivariate Signal Recovery in Computer Vision	Aug 12, 2020	Active LearningCollaborative Filtering	—Unverified	0
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the Loop	Aug 10, 2020	Active LearningEfficient Exploration	CodeCode Available	1
Deep Active Learning with Crowdsourcing Data for Privacy Policy Classification	Aug 7, 2020	Active LearningClassification	—Unverified	0
Cross-Model Image Annotation Platform with Active Learning	Aug 6, 2020	Active LearningBenchmarking	—Unverified	0

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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