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 661–670 of 3073 papers

Title	Date	Tasks	Status	Score
Bayesian Dark Knowledge	Jun 14, 2015	Active Learningparameter estimation	CodeCode Available	5
Adaptive Open-Set Active Learning with Distance-Based Out-of-Distribution Detection for Robust Task-Oriented Dialog System	Sep 1, 2024	Active LearningInformativeness	CodeCode Available	5
A Bayesian Approach for Sequence Tagging with Crowds	Nov 2, 2018	Active LearningArgument Mining	CodeCode Available	5
Bayesian Batch Active Learning as Sparse Subset Approximation	Aug 6, 2019	Active Learning	CodeCode Available	5
Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning	Apr 19, 2024	Active Learning	CodeCode Available	5
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals	Apr 20, 2020	Active LearningPseudo Label	CodeCode Available	5
DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software Ecosystem	Feb 25, 2024	Active Learningnamed-entity-recognition	CodeCode Available	5
SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection	May 24, 2022	Active LearningDomain Adaptation	CodeCode Available	5
Bayesian active learning for optimization and uncertainty quantification in protein docking	Jan 31, 2019	Active LearningBinary Classification	CodeCode Available	5
Active Learning for Non-Parametric Regression Using Purely Random Trees	Dec 1, 2018	Active LearningBinary Classification	CodeCode Available	5

Show:10 25 50

← PrevPage 67 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