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 1451–1460 of 3073 papers

Title	Date	Tasks	Status	Hype
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training	Dec 27, 2021	3D Pose EstimationActive Learning	CodeCode Available	1
Unsupervised Clustering Active Learning for Person Re-identification	Dec 26, 2021	Active LearningClustering	—Unverified	0
On the relationship between calibrated predictors and unbiased volume estimation	Dec 23, 2021	Active LearningImage Segmentation	CodeCode Available	0
Fair Active Learning: Solving the Labeling Problem in Insurance	Dec 17, 2021	Active LearningFairness	—Unverified	0
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language Models	Dec 16, 2021	Active LearningLanguage Modeling	CodeCode Available	1
Curriculum learning for data-driven modeling of dynamical systems	Dec 15, 2021	Active Learning	—Unverified	0
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort	Dec 15, 2021	Active LearningArticles	—Unverified	0
Towards General and Efficient Active Learning	Dec 15, 2021	Active LearningDepth Estimation	CodeCode Available	1
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework	Dec 14, 2021	Active LearningLanguage Modeling	—Unverified	0
Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not	Dec 13, 2021	Active Learning	—Unverified	0

Show:10 25 50

← PrevPage 146 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