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 2751–2760 of 3073 papers

Title	Date	Tasks	Status
Active model learning and diverse action sampling for task and motion planning	Mar 2, 2018	Active LearningMotion Planning	CodeCode Available
Investigating Multi-source Active Learning for Natural Language Inference	Feb 14, 2023	Active LearningNatural Language Inference	CodeCode Available
Active ML for 6G: Towards Efficient Data Generation, Acquisition, and Annotation	Jun 5, 2024	Active LearningComputational Efficiency	CodeCode Available
Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text Datasets	Oct 4, 2019	Active LearningBIG-bench Machine Learning	CodeCode Available
A Bibliographic View on Constrained Clustering	Sep 22, 2022	Active LearningClustering	CodeCode Available
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers	Aug 14, 2018	Active LearningGeneral Classification	CodeCode Available
Actively Learning Gaussian Process Dynamics	Nov 22, 2019	Active Learningregression	CodeCode Available
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation	Jul 20, 2023	Active LearningSuperpixels	CodeCode Available
Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning	Sep 21, 2022	Abusive LanguageActive Learning	CodeCode Available
Neural Predictive Monitoring under Partial Observability	Aug 16, 2021	Active LearningConformal Prediction	CodeCode Available

Show:10 25 50

← PrevPage 276 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