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 1661–1670 of 3073 papers

Title	Date	Tasks	Status
Addressing practical challenges in Active Learning via a hybrid query strategy	Oct 7, 2021	Active Learning	—Unverified
Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning	Oct 7, 2021	Active Learning	—Unverified
ActiveMatch: End-to-end Semi-supervised Active Representation Learning	Oct 6, 2021	Active LearningContrastive Learning	—Unverified
Active Learning for Contextual Search with Binary Feedbacks	Oct 3, 2021	Active LearningPAC learning	—Unverified
Automated Seed Quality Testing System using GAN & Active Learning	Oct 2, 2021	Active LearningGenerative Adversarial Network	CodeCode Available
OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis	Oct 1, 2021	Active Learningdocument understanding	—Unverified
Graph-boosted Active Learning for Multi-Source Entity Resolution	Sep 30, 2021	Active LearningEntity Resolution	CodeCode Available
Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow	Sep 30, 2021	Active LearningLiver Segmentation	—Unverified
Is Policy Learning Overrated?: Width-Based Planning and Active Learning for Atari	Sep 30, 2021	Active LearningAtari Games	CodeCode Available
Active Learning: Sampling in the Least Probable Disagreement Region	Sep 29, 2021	Active Learningvalid	—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