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 281–290 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
Active Pointly-Supervised Instance Segmentation	Jul 23, 2022	Active LearningInstance Segmentation	CodeCode Available	1	5
Cold-start Active Learning through Self-supervised Language Modeling	Oct 19, 2020	Active LearningClassification	CodeCode Available	1	5
Contextual Diversity for Active Learning	Aug 13, 2020	Active LearningDiversity	CodeCode Available	1	5
Detecting Underspecification with Local Ensembles	Oct 21, 2019	Active LearningOut-of-Distribution Detection	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
Active Learning for Open-set Annotation	Jan 18, 2022	Active Learning	CodeCode Available	1	5
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression	Nov 16, 2022	Active Learning	CodeCode Available	1	5
Accelerating high-throughput virtual screening through molecular pool-based active learning	Dec 13, 2020	Active LearningBayesian Optimization	CodeCode Available	1	5
Disentangled Multi-Fidelity Deep Bayesian Active Learning	May 7, 2023	Active LearningGaussian Processes	CodeCode Available	1	5
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene	Aug 26, 2020	Active Learning	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