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20102024
most citedGeometric deep learning: going beyond Euclidean data

3.7k citations · 7.1k across the 79 of their papers we have counts for

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Showing 2013Show all

6 papers · 1 filter

cs.CV2013★ 3 cited

Heat kernel coupling for multiple graph analysis

Michael M. Bronstein, Klaus Glashoff

In this paper, we introduce heat kernel coupling (HKC) as a method of constructing multimodal spectral geometry on weighted graphs of different size without vertex-wise bijective c…

cs.CV2013★ 17 cited

Sparse similarity-preserving hashing

Jonathan Masci, Alex M. Bronstein, Michael M. Bronstein +2

In recent years, a lot of attention has been devoted to efficient nearest neighbor search by means of similarity-preserving hashing. One of the plights of existing hashing techniqu…

cs.CV2013★ 1 cited

Structure-preserving color transformations using Laplacian commutativity

Davide Eynard, Artiom Kovnatsky, Michael M. Bronstein

Mappings between color spaces are ubiquitous in image processing problems such as gamut mapping, decolorization, and image optimization for color-blind people. Simple color transfo…

cs.CV2013★ 20 cited

Making Laplacians commute

Michael M. Bronstein, Klaus Glashoff, Terry A. Loring

In this paper, we construct multimodal spectral geometry by finding a pair of closest commuting operators (CCO) to a given pair of Laplacians. The CCOs are jointly diagonalizable a…

math.NA2013★ 1 cited

Asymptotic metrics on the space of matrices under the commutation relation

Klaus Glashoff, Michael M. Bronstein

We show that the norm of the commutator defines "almost a metric" on the quotient space of commuting matrices, in the sense that it is a semi-metric satisfying the triangle inequal…

math.NA2013★ 5 cited

Almost-commuting matrices are almost jointly diagonalizable

Klaus Glashoff, Michael M. Bronstein

We study the relation between approximate joint diagonalization of self-adjoint matrices and the norm of their commutator, and show that almost commuting self-adjoint matrices are…