6 citations · 24 across the 21 of their papers we have counts for
10 papers · 1 filter
Topological Trajectory Classification and Landmark Inference on Simplicial Complexes
Vincent P. Grande, Josef Hoppe, Florian Frantzen +1
We consider the problem of classifying trajectories on a discrete or discretised 2-dimensional manifold modelled by a simplicial complex. Previous works have proposed to project th…
TopoBench: A Framework for Benchmarking Topological Deep Learning
Lev Telyatnikov, Guillermo Bernardez, Marco Montagna +34
This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL). TopoBench decomposes TDL int…
Point-Level Topological Representation Learning on Point Clouds
Vincent P. Grande, Michael T. Schaub
Topological Data Analysis (TDA) allows us to extract powerful topological and higher-order information on the global shape of a data set or point cloud. Tools like Persistent Homol…
Random Abstract Cell Complexes
Josef Hoppe, Michael T. Schaub
We define a model for random (abstract) cell complexes (CCs), similiar to the well-known Erdős-Rényi model for graphs and its extensions for simplicial complexes. To build a random…
Learning From Simplicial Data Based on Random Walks and 1D Convolutions
Florian Frantzen, Michael T. Schaub
Triggered by limitations of graph-based deep learning methods in terms of computational expressivity and model flexibility, recent years have seen a surge of interest in computatio…
Ellipsoidal embeddings of graphs
Michaël Fanuel, Antoine Aspeel, Michael T. Schaub +1
Due to their flexibility to represent almost any kind of relational data, graph-based models have enjoyed a tremendous success over the past decades. While graphs are inherently on…
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein +19
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new fron…
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
Mustafa Hajij, Mathilde Papillon, Florian Frantzen +40
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: h…
A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings
Michael Scholkemper, Damin Kühn, Gerion Nabbefeld +3
Distance measures between graphs are important primitives for a variety of learning tasks. In this work, we describe an unsupervised, optimal transport based approach to define a d…
Faster optimal univariate microgaggregation
Felix I. Stamm, Michael T. Schaub
Microaggregation is a method to coarsen a dataset, by optimally clustering data points in groups of at least points, thereby providing a -anonymity type disclosure guarantee…