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Michael M. Bronstein
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3D Shape Analysis Through a Quantum Lens: the Average Mixing Kernel Signature
Orthogonalized Fourier polynomials for signal approximation and transfer
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
The Average Mixing Kernel Signature
Learning interaction patterns from surface representations of protein structure
GFrames: Gradient-based local reference frame for 3D shape matching
Functional maps representation on product manifolds
Isospectralization, or how to hear shape, style, and correspondence
Localized manifold harmonics for spectral shape analysis
Improved functional mappings via product preservation
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence
Efficient deformable shape correspondence via kernel matching
Spatial Maps: From low rank spectral to sparse spatial functional representations
Geometric deep learning on graphs and manifolds using mixture model CNNs
Fully spectral partial shape matching
Computing and Processing Correspondences with Functional Maps
Partial Functional Correspondence
Learning shape correspondence with anisotropic convolutional neural networks
Coupled functional maps
Shape analysis with anisotropic windowed Fourier transform
Non-rigid puzzles
Efficient globally optimal 2d-to-3d deformable shape matching
Anisotropic Diffusion Descriptors
Computing and Processing Correspondences with Functional Maps
Geometric Deep Learning
Matching deformable objects in clutter
SHREC'16: Partial matching of deformable shapes
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