4 citations · 5 across the 15 of their papers we have counts for
12 papers · 1 filter
Comparing Chatbot Performance Enhanced with Persistent Homology
Nithisha Raghavaraju, Barbara Giunti, Bastian Rieck
Chatbots have become increasingly prevalent across various domains, offering automated assistance in many areas, especially mental health support. The training is done using extrem…
LEAP: Local ECT-Based Learnable Positional Encodings for Graphs
Juan Amboage, Ernst Röell, Patrick Schnider +1
Graph neural networks (GNNs) largely rely on the message-passing paradigm, where nodes iteratively aggregate information from their neighbors. Yet, standard message passing neural…
Molecular Machine Learning Using Euler Characteristic Transforms
Victor Toscano-Duran, Florian Rottach, Bastian Rieck
The shape of a molecule determines its physicochemical and biological properties. However, it is often underrepresented in standard molecular representation learning approaches. He…
Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations
Mathieu Alain, So Takao, Xiaowen Dong +2
Predicting the labels of graph-structured data is crucial in scientific applications and is often achieved using graph neural networks (GNNs). However, when data is scarce, GNNs su…
Principal Curvatures Estimation with Applications to Single Cell Data
Yanlei Zhang, Lydia Mezrag, Xingzhi Sun +6
The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datasets. A common method in manifold learnin…
Topology meets Machine Learning: An Introduction using the Euler Characteristic Transform
Bastian Rieck
This overview article makes the case for how topological concepts can enrich research in machine learning. Using the Euler Characteristic Transform (ECT), a geometrical-topological…
Graph Classification Gaussian Processes via Hodgelet Spectral Features
Mathieu Alain, So Takao, Xiaowen Dong +2
The problem of classifying graphs is ubiquitous in machine learning. While it is standard to apply graph neural networks or graph kernel methods, Gaussian processes can be employed…
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms
Julius von Rohrscheidt, Bastian Rieck
The Euler Characteristic Transform (ECT) is an efficiently-computable geometrical-topological invariant that characterizes the global shape of data. In this paper, we introduce the…
MANTRA: The Manifold Triangulations Assemblage
Rubén Ballester, Ernst Röell, Daniel Bīn Schmid +4
The rising interest in leveraging higher-order interactions present in complex systems has led to a surge in more expressive models exploiting higher-order structures in the data,…
CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs
Davide Buffelli, Farzin Soleymani, Bastian Rieck
Graph neural networks have become the default choice by practitioners for graph learning tasks such as graph classification and node classification. Nevertheless, popular graph neu…
The Manifold Density Function: An Intrinsic Method for the Validation of Manifold Learning
Benjamin Holmgren, Eli Quist, Jordan Schupbach +2
We introduce the manifold density function, which is an intrinsic method to validate manifold learning techniques. Our approach adapts and extends Ripley's -function, and catego…
Image analysis for Alzheimer's disease prediction: Embracing pathological hallmarks for model architecture design
Sarah C. Brüningk, Felix Hensel, Catherine R. Jutzeler +1
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 str…