SOTAVerified|Agents Browse Leaderboard About

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 751–760 of 3073 papers

Title	Date	Tasks	Status	Score
A Comparison of Strategies for Source-Free Domain Adaptation	May 1, 2022	Active LearningData Augmentation	CodeCode Available	5
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers	Aug 14, 2018	Active LearningGeneral Classification	CodeCode Available	5
Bayesian Dark Knowledge	Jun 14, 2015	Active Learningparameter estimation	CodeCode Available	5
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture	May 22, 2023	Active LearningDecision Making	CodeCode Available	5
Anytime Active Learning	Jul 27, 2014	Active LearningDocument Classification	CodeCode Available	5
Active Learning of Molecular Data for Task-Specific Objectives	Aug 20, 2024	Active LearningGaussian Processes	CodeCode Available	5
A-Optimal Active Learning	Oct 18, 2021	Active LearningExperimental Design	CodeCode Available	5
Interactive Refinement of Cross-Lingual Word Embeddings	Nov 8, 2019	Active LearningCross-Lingual Word Embeddings	CodeCode Available	5
Active Keyword Selection to Track Evolving Topics on Twitter	Sep 22, 2022	Active Learning	CodeCode Available	5
Active Learning for Neural Machine Translation	Dec 30, 2022	Active LearningMachine Translation	CodeCode Available	5

Show:10 25 50

← PrevPage 76 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