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 2341–2350 of 3073 papers

Title	Date	Tasks	Status
A Survey on Deep Learning of Small Sample in Biomedical Image Analysis	Aug 1, 2019	Active LearningData Augmentation	CodeCode Available
Incremental Domain Adaptation for Neural Machine Translation in Low-Resource Settings	Aug 1, 2019	Active LearningDomain Adaptation	CodeCode Available
FDive: Learning Relevance Models using Pattern-based Similarity Measures	Jul 29, 2019	Active Learningfeature selection	—Unverified
Mindful Active Learning	Jul 28, 2019	Active LearningActivity Recognition	CodeCode Available
Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling	Jul 21, 2019	Active LearningOrgan Segmentation	CodeCode Available
Photonic architecture for reinforcement learning	Jul 17, 2019	Active LearningQ-Learning	—Unverified
Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning	Jul 17, 2019	Active LearningRetrieval	—Unverified
Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples	Jul 17, 2019	Active LearningExperimental Design	—Unverified
Modeling Human Annotation Errors to Design Bias-Aware Systems for Social Stream Processing	Jul 16, 2019	Active LearningBIG-bench Machine Learning	—Unverified
MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation	Jul 16, 2019	Active Learningnamed-entity-recognition	—Unverified

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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