Karsten Borgwardt | DeepAI

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∙ 05/12/2023

Fisher Information Embedding for Node and Graph Learning

Attention-based graph neural networks (GNNs), such as graph attention ne...

0 Dexiong Chen, et al. ∙

∙ 07/06/2022

Unsupervised Manifold Alignment with Joint Multidimensional Scaling

We introduce Joint Multidimensional Scaling, a novel approach for unsupe...

0 Dexiong Chen, et al. ∙

∙ 06/02/2022

Approximate Network Motif Mining Via Graph Learning

Frequent and structurally related subgraphs, also known as network motif...

0 Carlos Oliver, et al. ∙

∙ 02/07/2022

Structure-Aware Transformer for Graph Representation Learning

The Transformer architecture has gained growing attention in graph repre...

0 Dexiong Chen, et al. ∙

∙ 12/18/2021

Weisfeiler and Leman go Machine Learning: The Story so far

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

65 Christopher Morris, et al. ∙

∙ 10/28/2021

The magnitude vector of images

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

10 Michael F Adamer, et al. ∙

∙ 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...

2 Michael Moor, et al. ∙

∙ 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. ...

51 Leslie O'Bray, et al. ∙

∙ 02/15/2021

Topological Graph Neural Networks

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

68 Max Horn, et al. ∙

∙ 11/07/2020

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

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

95 Karsten Borgwardt, et al. ∙

∙ 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...

23 Jannis Born, et al. ∙

∙ 06/14/2020

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

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

0 Bastian Rieck, et al. ∙

∙ 05/25/2020

Path Imputation Strategies for Signature Models

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

10 Michael Moor, et al. ∙

∙ 09/26/2019

Set Functions for Time Series

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

0 Max Horn, et al. ∙

∙ 06/04/2019

Wasserstein Weisfeiler-Lehman Graph Kernels

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

0 Matteo Togninalli, et al. ∙

∙ 06/03/2019

Topological Autoencoders

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

1 Michael Moor, et al. ∙

∙ 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...

0 Stephanie L. Hyland, et al. ∙

∙ 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...

0 Michael Moor, et al. ∙

∙ 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 ...

16 Bastian Rieck, et al. ∙

∙ 08/24/2015

Searching for significant patterns in stratified data

Significant pattern mining, the problem of finding itemsets that are sig...

0 Felipe Llinares-Lopez, et al. ∙

∙ 03/29/2013

Geometric tree kernels: Classification of COPD from airway tree geometry

Methodological contributions: This paper introduces a family of kernels ...

0 Aasa Feragen, et al. ∙

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