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 1241–1250 of 3073 papers

Title	Date	Tasks	Status
Active Learning for Neural Machine Translation	Dec 30, 2022	Active LearningMachine Translation	CodeCode Available
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection	Dec 28, 2022	Active LearningChange Detection	—Unverified
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning	Dec 28, 2022	Active LearningSelf-Supervised Learning	—Unverified
Gaussian Process Classification Bandits	Dec 26, 2022	Active LearningClassification	—Unverified
Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders	Dec 26, 2022	Active Learningregression	—Unverified
An active learning method for solving competitive multi-agent decision-making and control problems	Dec 23, 2022	Active LearningDecision Making	CodeCode Available
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection	Dec 21, 2022	Active LearningObject	—Unverified
Temporal Output Discrepancy for Loss Estimation-based Active Learning	Dec 20, 2022	Active Learningimage-classification	—Unverified
Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems	Dec 20, 2022	Active LearningCollaborative Filtering	CodeCode Available
Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis	Dec 20, 2022	Active Learning	—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