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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
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning	Jun 22, 2021	Active Learning	CodeCode Available	1
A Simple Baseline for Low-Budget Active Learning	Oct 22, 2021	Active Learningimage-classification	CodeCode Available	1
Bayesian Active Learning with Fully Bayesian Gaussian Processes	May 20, 2022	Active LearningGaussian Processes	CodeCode Available	1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples	Dec 3, 2021	Active LearningDeep Learning	CodeCode Available	1
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language Models	Dec 16, 2021	Active LearningLanguage Modeling	CodeCode Available	1
Active Learning for Open-set Annotation	Jan 18, 2022	Active Learning	CodeCode Available	1
Active Learning for Optimal Intervention Design in Causal Models	Sep 10, 2022	Active LearningExperimental Design	CodeCode Available	1
Accelerating high-throughput virtual screening through molecular pool-based active learning	Dec 13, 2020	Active LearningBayesian Optimization	CodeCode Available	1
Bayesian Model-Agnostic Meta-Learning	Jun 11, 2018	Active Learningimage-classification	CodeCode Available	1
Consistency-based Active Learning for Object Detection	Mar 18, 2021	Active LearningClassification	CodeCode Available	1

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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