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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 1541–1550 of 3073 papers

Title	Date	Tasks	Status	Hype
Bayesian Active Summarization	Oct 9, 2021	Active LearningText Summarization	—Unverified	0
Class-Balanced Active Learning for Image Classification	Oct 9, 2021	Active LearningClassification	CodeCode Available	1
Synthesizing Video Trajectory Queries	Oct 8, 2021	Active LearningObject Tracking	—Unverified	0
Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations	Oct 8, 2021	Active Learning	—Unverified	0
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up	Oct 8, 2021	Active LearningBIG-bench Machine Learning	—Unverified	0
Active learning for interactive satellite image change detection	Oct 8, 2021	Active LearningChange Detection	—Unverified	0
Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning	Oct 7, 2021	Active Learning	—Unverified	0
Addressing practical challenges in Active Learning via a hybrid query strategy	Oct 7, 2021	Active Learning	—Unverified	0
Hitting the Target: Stopping Active Learning at the Cost-Based Optimum	Oct 7, 2021	Active Learning	CodeCode Available	1
Unsupervised Selective Labeling for More Effective Semi-Supervised Learning	Oct 6, 2021	Active LearningSemi-Supervised Image Classification (Cold Start)	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