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 181–190 of 3073 papers

Title	Date	Tasks	Status	Hype	Score
Deep Active Learning for Named Entity Recognition	Jul 19, 2017	Active LearningDecoder	CodeCode Available	1	5
Active Sensing for Communications by Learning	Dec 8, 2021	Active Learning	CodeCode Available	1	5
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain Knowledge	Jun 1, 2020	Active LearningDomain Adaptation	CodeCode Available	1	5
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation	Feb 14, 2022	Active Learning	CodeCode Available	1	5
Active Learning for Coreference Resolution using Discrete Annotation	Apr 28, 2020	Active LearningClustering	CodeCode Available	1	5
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds	Jun 9, 2019	Active LearningDiversity	CodeCode Available	1	5
Active Learning for Convolutional Neural Networks: A Core-Set Approach	Aug 1, 2017	Active Learningimage-classification	CodeCode Available	1	5
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models	Jul 1, 2022	Active Learningtext-classification	CodeCode Available	1	5
Active WeaSuL: Improving Weak Supervision with Active Learning	Apr 30, 2021	Active Learning	CodeCode Available	1	5
Active Learning for Deep Object Detection via Probabilistic Modeling	Mar 30, 2021	Active LearningClassification	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