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 61–70 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages	Nov 7, 2022	Active LearningLanguage Modeling	CodeCode Available	1	5
Active Learning for Domain Adaptation: An Energy-Based Approach	Dec 2, 2021	Active LearningDomain Adaptation	CodeCode Available	1	5
Enhanced spatio-temporal electric load forecasts using less data with active deep learning	Dec 8, 2020	Active LearningBIG-bench Machine Learning	CodeCode Available	1	5
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	5
ActiveNeRF: Learning where to See with Uncertainty Estimation	Sep 18, 2022	Active LearningNeRF	CodeCode Available	1	5
Active Imitation Learning with Noisy Guidance	May 26, 2020	Active LearningImitation Learning	CodeCode Available	1	5
Active Invariant Causal Prediction: Experiment Selection through Stability	Jun 10, 2020	Active LearningPrediction	CodeCode Available	1	5
Active learning based generative design for the discovery of wide bandgap materials	Feb 28, 2021	Active LearningBand Gap	CodeCode Available	1	5
Active Pointly-Supervised Instance Segmentation	Jul 23, 2022	Active LearningInstance Segmentation	CodeCode Available	1	5
Active Learning Strategies for Weakly-supervised Object Detection	Jul 25, 2022	Active LearningObject	CodeCode Available	1	5

Show:10 25 50

← PrevPage 7 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