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 1041–1050 of 3073 papers

Title	Date	Tasks	Status	Hype
Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems	Dec 20, 2022	Active LearningCollaborative Filtering	CodeCode Available	0
POTATO: The Portable Text Annotation Tool	Dec 16, 2022	Active Learningtext annotation	CodeCode Available	2
Man-recon: manifold learning for reconstruction with deep autoencoder for smart seismic interpretation	Dec 15, 2022	Active LearningSeismic Interpretation	CodeCode Available	1
THMA: Tencent HD Map AI System for Creating HD Map Annotations	Dec 14, 2022	Active LearningWeakly-supervised Learning	—Unverified	0
The Infinite Index: Information Retrieval on Generative Text-To-Image Models	Dec 14, 2022	Active LearningGame Design	—Unverified	0
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection	Dec 13, 2022	Active LearningAdversarial Attack	—Unverified	0
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data	Dec 13, 2022	Active Learning	—Unverified	0
An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision	Dec 12, 2022	Active LearningTransfer Learning	—Unverified	0
MAViC: Multimodal Active Learning for Video Captioning	Dec 11, 2022	Active LearningDecoder	—Unverified	0
Predicting article quality scores with machine learning: The UK Research Excellence Framework	Dec 11, 2022	Active LearningArticles	—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