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 171–180 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
Active Learning by Acquiring Contrastive Examples	Sep 8, 2021	Active LearningDiversity	CodeCode Available	1	5
Active Learning by Feature Mixing	Mar 14, 2022	Active Learning	CodeCode Available	1	5
Unsupervised Selective Labeling for More Effective Semi-Supervised Learning	Oct 6, 2021	Active LearningSemi-Supervised Image Classification (Cold Start)	CodeCode Available	1	5
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations	Dec 2, 2023	Active LearningGaussian Processes	CodeCode Available	1	5
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction	Jul 7, 2020	Active LearningGraph Neural Network	CodeCode Available	1	5
DEAL: Difficulty-aware Active Learning for Semantic Segmentation	Oct 17, 2020	Active LearningSegmentation	CodeCode Available	1	5
Active Pointly-Supervised Instance Segmentation	Jul 23, 2022	Active LearningInstance Segmentation	CodeCode Available	1	5
ActiveNeRF: Learning where to See with Uncertainty Estimation	Sep 18, 2022	Active LearningNeRF	CodeCode Available	1	5
Active Learning for Computationally Efficient Distribution of Binary Evolution Simulations	Mar 30, 2022	Active Learningregression	CodeCode Available	1	5
Active Learning for Convolutional Neural Networks: A Core-Set Approach	Aug 1, 2017	Active Learningimage-classification	CodeCode Available	1	5

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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