Deformable Protein Shape Classification Based on Deep Learning, and the Fractional Fokker–Planck and Kähler–Dirac Equations
Author:
Affiliation:
1. National Research Council, Ottawa, ON, Canada
2. University of Ottawa, Ottawa, ON, Canada
3. Umanis Research and Innovation, Levallois-Perret, France
Funder
National Research Council Canada
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software
Link
http://xplorestaging.ieee.org/ielx7/34/9970415/09695227.pdf?arnumber=9695227
Reference101 articles.
1. Learned Binary Spectral Shape Descriptor for 3D Shape Correspondence
2. Learning Spectral Descriptors for Deformable Shape Correspondence
3. The wave kernel signature: A quantum mechanical approach to shape analysis
4. A Concise and Provably Informative Multi-Scale Signature Based on Heat Diffusion
5. Laplace-Beltrami Eigenfunctions Towards an Algorithm That "Understands" Geometry
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