b'Bastian Rieck'

research

∙ 10/21/2022

Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework

Bridging geometry and topology, curvature is a powerful and expressive i...

Corinna Coupette, et al. ∙

research

∙ 09/30/2022

TOAST: Topological Algorithm for Singularity Tracking

The manifold hypothesis, which assumes that data lie on or close to an u...

Julius von Rohrscheidt, et al. ∙

research

∙ 08/30/2022

A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images

Diffusion models are a class of generative models, showing superior perf...

Dominik J. E. Waibel, et al. ∙

research

∙ 06/16/2022

On the Surprising Behaviour of node2vec

Graph embedding techniques are a staple of modern graph learning researc...

Celia Hacker, et al. ∙

research

∙ 06/16/2022

All the World's a (Hyper)Graph: A Data Drama

We introduce Hyperbard, a dataset of diverse relational data representat...

Corinna Coupette, et al. ∙

research

∙ 06/15/2022

Taxonomy of Benchmarks in Graph Representation Learning

Graph Neural Networks (GNNs) extend the success of neural networks to gr...

Renming Liu, et al. ∙

research

∙ 06/08/2022

Diffusion Curvature for Estimating Local Curvature in High Dimensional Data

We introduce a new intrinsic measure of local curvature on point-cloud d...

Dhananjay Bhaskar, et al. ∙

research

∙ 03/28/2022

Time-inhomogeneous diffusion geometry and topology

Diffusion condensation is a dynamic process that yields a sequence of mu...

Guillaume Huguet, et al. ∙

research

∙ 03/03/2022

Capturing Shape Information with Multi-Scale Topological Loss Terms for 3D Reconstruction

Reconstructing 3D objects from 2D images is both challenging for our bra...

Dominik J. E. Waibel, et al. ∙

research

∙ 12/18/2021

Weisfeiler and Leman go Machine Learning: The Story so far

In recent years, algorithms and neural architectures based on the Weisfe...

Christopher Morris, et al. ∙

research

∙ 11/15/2021

Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease Classification

Owing to its pristine soft-tissue contrast and high resolution, structur...

Merel Kuijs, et al. ∙

research

∙ 10/28/2021

The magnitude vector of images

The magnitude of a finite metric space is a recently-introduced invarian...

Michael F Adamer, et al. ∙

research

∙ 10/27/2021

Towards a Taxonomy of Graph Learning Datasets

Graph neural networks (GNNs) have attracted much attention due to their ...

Renming Liu, et al. ∙

research

∙ 07/12/2021

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning

Despite decades of clinical research, sepsis remains a global public hea...

Michael Moor, et al. ∙

research

∙ 06/02/2021

Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions

Graph generative models are a highly active branch of machine learning. ...

Leslie O'Bray, et al. ∙

research

∙ 04/25/2021

Basic Analysis of Bin-Packing Heuristics

The bin-packing problem continues to remain relevant in numerous applica...

Bastian Rieck, et al. ∙

research

∙ 02/15/2021

Topological Graph Neural Networks

Graph neural networks (GNNs) are a powerful architecture for tackling gr...

Max Horn, et al. ∙

research

∙ 11/22/2020

Topological Data Analysis of copy number alterations in cancer

Identifying subgroups and properties of cancer biopsy samples is a cruci...

Stefan Groha, et al. ∙

research

∙ 11/12/2020

Image analysis for Alzheimer's disease prediction: Embracing pathological hallmarks for model architecture design

Alzheimer's disease (AD) is associated with local (e.g. brain tissue atr...

Sarah C. Brüningk, et al. ∙

research

∙ 11/07/2020

Graph Kernels: State-of-the-Art and Future Challenges

Graph-structured data are an integral part of many application domains, ...

Karsten Borgwardt, et al. ∙

research

∙ 09/13/2020

Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis

Controlling the COVID-19 pandemic largely hinges upon the existence of f...

Jannis Born, et al. ∙

research

∙ 06/14/2020

Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence

Functional magnetic resonance imaging (fMRI) is a crucial technology for...

Bastian Rieck, et al. ∙

research

∙ 05/25/2020

Path Imputation Strategies for Signature Models

The signature transform is a 'universal nonlinearity' on the space of co...

Michael Moor, et al. ∙

research

∙ 09/26/2019

Set Functions for Time Series

Despite the eminent successes of deep neural networks, many architecture...

Max Horn, et al. ∙

research

∙ 07/31/2019

Topological Machine Learning with Persistence Indicator Functions

Techniques from computational topology, in particular persistent homolog...

Bastian Rieck, et al. ∙

research

∙ 07/31/2019

Persistent Intersection Homology for the Analysis of Discrete Data

Topological data analysis is becoming increasingly relevant to support t...

Bastian Rieck, et al. ∙

research

∙ 06/04/2019

Wasserstein Weisfeiler-Lehman Graph Kernels

Graph kernels are an instance of the class of R-Convolution kernels, whi...

Matteo Togninalli, et al. ∙

research

∙ 06/03/2019

Topological Autoencoders

We propose a novel approach for preserving topological structures of the...

Michael Moor, et al. ∙

research

∙ 04/16/2019

Machine learning for early prediction of circulatory failure in the intensive care unit

Intensive care clinicians are presented with large quantities of patient...

Stephanie L. Hyland, et al. ∙

research

∙ 02/05/2019

Temporal Convolutional Networks and Dynamic Time Warping can Drastically Improve the Early Prediction of Sepsis

Motivation: Sepsis is a life-threatening host response to infection asso...

Michael Moor, et al. ∙

research

∙ 12/23/2018

Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology

While many approaches to make neural networks more fathomable have been ...

Bastian Rieck, et al. ∙

Bastian Rieck

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