Learnable Filters for Geometric Scattering Modules-Reference-Cited by-同舟云学术

Learnable Filters for Geometric Scattering Modules

Published:2024 Issue: Volume:72 Page:2939-2952
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Tong Alexander¹^ORCID,Wenkel Frederik²^ORCID,Bhaskar Dhananjay³^ORCID,Macdonald Kincaid⁴^ORCID,Grady Jackson⁵^ORCID,Perlmutter Michael⁶^ORCID,Krishnaswamy Smita⁷^ORCID,Wolf Guy²^ORCID

Affiliation:

1. Department of Computer Science and Operations Research, Université de Montréal, Montreal, QC, Canada

2. Department of Mathematics and Statistics, Université de Montréal, Montreal, QC, Canada

3. Department of Genetics, Yale University, New Haven, CT, USA

4. Department of Mathematics, Yale University, New Haven, CT, USA

5. Department of Computer Science, Yale University, New Haven, CT, USA

6. Department of Mathematics, University of California, Los Angeles

7. Department of Genetics and Department of Computer Science, Yale University, New Haven, CT, USA

Funder

Fin-ML CREATE Graduate Studies Scholarship for Ph.D.

J.A. DeSève Scholarship for Ph.D.

Yale-Boehringer Ingelheim Biomedical Data Science Fellowship

Chan Zuckerberg Initiative

NSF Career

Sloan Fellowship

CIFAR AI Chair

Institut de Valorisation des Données

Fonds de recherche du Québec – Nature et technologies

NSERC Discovery

NSF

NIH

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/78/10347386/10473218-aam.pdf

Reference47 articles.

1. Geometric Deep Learning: Going beyond Euclidean data

2. Spectral networks and locally connected networks on graphs;Bruna,2014

3. Convolutional neural networks on graphs with fast localized spectral filtering;Defferrard,2016

4. Semi-supervised classification with graph convolutional networks;Kipf,2016

5. Inductive representa-tion learning on large graphs;Hamilton,2017