Learning Block Structured Graphs in Gaussian Graphical Models

Colombi, Alessandro; Argiento, Raffaele; Paci, Lucia; Pini, Alessia

doi:10.1080/10618600.2023.2210184

Journal of Computational and Graphical Statistics

2023

DOI: 10.1080/10618600.2023.2210184

|View full text |Cite

Learning Block Structured Graphs in Gaussian Graphical Models

Alessandro Colombi

Raffaele Argiento

Lucia Paci

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Laplacian attention: A plug‐and‐play algorithm without increasing model complexity for vision tasks

Chen,

Lu,

Wen

2024

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

Most prevailing attention mechanism modules in contemporary research are convolution‐based modules, and while these modules contribute to enhancing the accuracy of deep learning networks in visual tasks, they concurrently augment the overall model complexity. To address the problem, this paper proposes a plug‐and‐play algorithm that does not increase the complexity of the model, Laplacian attention (LA). The LA algorithm first calculates the similarity distance between feature points in the feature space and feature channel and constructs the residual Laplacian matrix between feature points through the similarity distance and Gaussian kernel. This construction serves to segregate non‐similar feature points while aggregating those with similarities. Ultimately, the LA algorithm allocates the outputs of the feature channel and the feature space adaptively to derive the final LA outputs. Crucially, the LA algorithm is confined to the forward computation process and does not involve backpropagation or any parameter learning. The LA algorithm undergoes comprehensive experimentation on three distinct datasets—namely Cifar‐10, miniImageNet, and Pascal VOC 2012. The experimental results demonstrate that, compared with the advanced attention mechanism modules in recent years, such as SENet, CBAM, ECANet, coordinate attention, and triplet attention, the LA algorithm exhibits superior performance across image classification, object detection and semantic segmentation tasks.

show abstract

Laplacian attention: A plug‐and‐play algorithm without increasing model complexity for vision tasks

Chen,

Lu,

Wen

2024

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Learning Block Structured Graphs in Gaussian Graphical Models

Cited by 1 publication

References 40 publications

Laplacian attention: A plug‐and‐play algorithm without increasing model complexity for vision tasks

Laplacian attention: A plug‐and‐play algorithm without increasing model complexity for vision tasks

Contact Info

Product

Resources

About