Search Results for author: Bastian Rieck

Found 32 papers, 18 papers with code

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

1 code implementation16 Jun 2022 Corinna Coupette, Jilles Vreeken, Bastian Rieck

We introduce Hyperbard, a dataset of diverse relational data representations derived from Shakespeare's plays.

Graph Learning Graph Mining

On the Surprising Behaviour of node2vec

1 code implementation16 Jun 2022 Celia Hacker, Bastian Rieck

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

Graph Embedding Graph Learning

Time-inhomogeneous diffusion geometry and topology

no code implementations28 Mar 2022 Guillaume Huguet, Alexander Tong, Bastian Rieck, Jessie Huang, Manik Kuchroo, Matthew Hirn, Guy Wolf, Smita Krishnaswamy

From a geometric perspective, we obtain convergence bounds based on the smallest transition probability and the radius of the data, whereas from a spectral perspective, our bounds are based on the eigenspectrum of the diffusion kernel.

Denoising Topological Data Analysis

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

1 code implementation3 Mar 2022 Dominik J. E. Waibel, Scott Atwell, Matthias Meier, Carsten Marr, Bastian Rieck

We propose to complement geometrical shape information by including multi-scale topological features, such as connected components, cycles, and voids, in the reconstruction loss.

3D Reconstruction

Weisfeiler and Leman go Machine Learning: The Story so far

no code implementations18 Dec 2021 Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten Borgwardt

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, emerged as a powerful tool for machine learning with graphs and relational data.

Representation Learning

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

no code implementations15 Nov 2021 Merel Kuijs, Catherine R. Jutzeler, Bastian Rieck, Sarah C. Brüningk

Owing to its pristine soft-tissue contrast and high resolution, structural magnetic resonance imaging (MRI) is widely applied in neurology, making it a valuable data source for image-based machine learning (ML) and deep learning applications.

Towards a Taxonomy of Graph Learning Datasets

no code implementations27 Oct 2021 Renming Liu, Semih Cantürk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek

Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data.

Graph Learning

Topological Graph Neural Networks

1 code implementation ICLR 2022 Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, Karsten Borgwardt

Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles.

Graph Learning Node Classification

Exploring the Geometry and Topology of Neural Network Loss Landscapes

no code implementations31 Jan 2021 Stefan Horoi, Jessie Huang, Bastian Rieck, Guillaume Lajoie, Guy Wolf, Smita Krishnaswamy

This suggests that qualitative and quantitative examination of the loss landscape geometry could yield insights about neural network generalization performance during training.

Dimensionality Reduction

Topological Data Analysis of copy number alterations in cancer

no code implementations22 Nov 2020 Stefan Groha, Caroline Weis, Alexander Gusev, Bastian Rieck

Identifying subgroups and properties of cancer biopsy samples is a crucial step towards obtaining precise diagnoses and being able to perform personalized treatment of cancer patients.

Topological Data Analysis

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

no code implementations12 Nov 2020 Sarah C. Brüningk, Felix Hensel, Catherine R. Jutzeler, Bastian Rieck

Alzheimer's disease (AD) is associated with local (e. g. brain tissue atrophy) and global brain changes (loss of cerebral connectivity), which can be detected by high-resolution structural magnetic resonance imaging.

Disease Prediction

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

1 code implementation7 Nov 2020 Karsten Borgwardt, Elisabetta Ghisu, Felipe Llinares-López, Leslie O'Bray, Bastian Rieck

Graph-structured data are an integral part of many application domains, including chemoinformatics, computational biology, neuroimaging, and social network analysis.

Challenging Euclidean Topological Autoencoders

1 code implementation NeurIPS Workshop TDA_and_Beyond 2020 Michael Moor, Max Horn, Karsten Borgwardt, Bastian Rieck

Topological autoencoders (TopoAE) have demonstrated their capabilities for performing dimensionality reduction while at the same time preserving topological information of the input space.

Dimensionality Reduction

Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis

2 code implementations13 Sep 2020 Jannis Born, Nina Wiedemann, Gabriel Brändle, Charlotte Buhre, Bastian Rieck, Karsten Borgwardt

Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools.

Temporal Localization

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

1 code implementation NeurIPS 2020 Bastian Rieck, Tristan Yates, Christian Bock, Karsten Borgwardt, Guy Wolf, Nicholas Turk-Browne, Smita Krishnaswamy

We observe significant differences in both brain state trajectories and overall topological activity between adults and children watching the same movie.

Path Imputation Strategies for Signature Models of Irregular Time Series

2 code implementations25 May 2020 Michael Moor, Max Horn, Christian Bock, Karsten Borgwardt, Bastian Rieck

The signature transform is a 'universal nonlinearity' on the space of continuous vector-valued paths, and has received attention for use in machine learning on time series.

Imputation Time Series

Set Functions for Time Series

1 code implementation ICML 2020 Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten Borgwardt

Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications.

Time Series Time Series Classification

Persistent Intersection Homology for the Analysis of Discrete Data

no code implementations31 Jul 2019 Bastian Rieck, Markus Banagl, Filip Sadlo, Heike Leitte

Topological data analysis is becoming increasingly relevant to support the analysis of unstructured data sets.

Topological Data Analysis

Topological Machine Learning with Persistence Indicator Functions

no code implementations31 Jul 2019 Bastian Rieck, Filip Sadlo, Heike Leitte

Techniques from computational topology, in particular persistent homology, are becoming increasingly relevant for data analysis.

Topological Data Analysis

Wasserstein Weisfeiler-Lehman Graph Kernels

1 code implementation NeurIPS 2019 Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten Borgwardt

Most graph kernels are an instance of the class of $\mathcal{R}$-Convolution kernels, which measure the similarity of objects by comparing their substructures.

Graph Classification

Topological Autoencoders

2 code implementations ICML 2020 Michael Moor, Max Horn, Bastian Rieck, Karsten Borgwardt

We propose a novel approach for preserving topological structures of the input space in latent representations of autoencoders.

Topological Data Analysis

Graph Filtration Learning

1 code implementation ICML 2020 Christoph D. Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, Roland Kwitt

We propose an approach to learning with graph-structured data in the problem domain of graph classification.

General Classification Graph Classification

Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping

2 code implementations5 Feb 2019 Michael Moor, Max Horn, Bastian Rieck, Damian Roqueiro, Karsten Borgwardt

This empirical study proposes two novel approaches for the early detection of sepsis: a deep learning model and a lazy learner based on time series distances.

Dynamic Time Warping Time Series +1

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