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 711–720 of 3073 papers

Title	Date	Tasks	Status	Hype
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition	Sep 30, 2023	Active LearningEmotion Recognition	—Unverified	0
Towards Free Data Selection with General-Purpose Models	Sep 29, 2023	Active Learning	CodeCode Available	1
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression	Sep 29, 2023	Active LearningGaussian Processes	—Unverified	0
Gradient and Uncertainty Enhanced Sequential Sampling for Global Fit	Sep 29, 2023	Active LearningExperimental Design	CodeCode Available	0
Multi-Resolution Active Learning of Fourier Neural Operators	Sep 29, 2023	Active LearningLEMMA	CodeCode Available	0
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling	Sep 28, 2023	Active LearningDiversity	—Unverified	0
Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory Exploration	Sep 28, 2023	Active LearningGaussian Processes	CodeCode Available	0
Frugal Satellite Image Change Detection with Deep-Net Inversion	Sep 26, 2023	Active LearningChange Detection	—Unverified	0
Online Active Learning For Sound Event Detection	Sep 25, 2023	Active LearningEvent Detection	—Unverified	0
Discwise Active Learning for LiDAR Semantic Segmentation	Sep 23, 2023	Active LearningLIDAR Semantic Segmentation	—Unverified	0

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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