Rethinking graph transformers with spectral attention
In recent years, the Transformer architecture has proven to be very successful in sequence
processing, but its application to other data structures, such as graphs, has remained limited …
processing, but its application to other data structures, such as graphs, has remained limited …
Understanding oversquashing in gnns through the lens of effective resistance
Message passing graph neural networks (GNNs) are a popular learning architectures for
graph-structured data. However, one problem GNNs experience is oversquashing, where a …
graph-structured data. However, one problem GNNs experience is oversquashing, where a …
Riemannian flow matching on general geometries
Registration of 3D point clouds and meshes: A survey from rigid to nonrigid
Three-dimensional surface registration transforms multiple three-dimensional data sets into
the same coordinate system so as to align overlap** components of these sets. Recent …
the same coordinate system so as to align overlap** components of these sets. Recent …
A survey on shape correspondence
We review methods designed to compute correspondences between geometric shapes
represented by triangle meshes, contours or point sets. This survey is motivated in part by …
represented by triangle meshes, contours or point sets. This survey is motivated in part by …
Geodesics in heat: A new approach to computing distance based on heat flow
We introduce the heat method for computing the geodesic distance to a specified subset (eg,
point or curve) of a given domain. The heat method is robust, efficient, and simple to …
point or curve) of a given domain. The heat method is robust, efficient, and simple to …
Spatially aggregating spectral descriptors for nonrigid 3D shape retrieval: a comparative survey
This paper presents a comprehensive review and analysis of recent spectral shape
descriptors for nonrigid 3D shape retrieval. More specifically, we compare the latest spectral …
descriptors for nonrigid 3D shape retrieval. More specifically, we compare the latest spectral …
Hunt for the unique, stable, sparse and fast feature learning on graphs
For the purpose of learning on graphs, we hunt for a graph feature representation that
exhibit certain uniqueness, stability and sparsity properties while also being amenable to …
exhibit certain uniqueness, stability and sparsity properties while also being amenable to …
The heat method for distance computation
We introduce the heat method for solving the single-or multiple-source shortest path
problem on both flat and curved domains. A key insight is that distance computation can be …
problem on both flat and curved domains. A key insight is that distance computation can be …