no code implementations • 14 Feb 2024 • Theodore Papamarkou, Tolga Birdal, Michael Bronstein, Gunnar Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Liò, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Veličković, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models.

no code implementations • 14 Feb 2024 • Benjamin Holmgren, Eli Quist, Jordan Schupbach, Brittany Terese Fasy, Bastian Rieck

We introduce the manifold density function, which is an intrinsic method to validate manifold learning techniques.

no code implementations • 2 Feb 2024 • Jeremy Wayland, Corinna Coupette, Bastian Rieck

Echoing recent calls to counter reliability and robustness concerns in machine learning via multiverse analysis, we present PRESTO, a principled framework for mapping the multiverse of machine-learning models that rely on latent representations.

1 code implementation • 13 Dec 2023 • Kelly Maggs, Celia Hacker, Bastian Rieck

In this paper, we take a different approach, focusing on leveraging geometric information from simplicial complexes embedded in $\mathbb{R}^n$ using node coordinates.

no code implementations • 27 Nov 2023 • Katharina Limbeck, Rayna Andreeva, Rik Sarkar, Bastian Rieck

We develop a family of magnitude-based measures of the intrinsic diversity of latent representations, formalising a novel notion of dissimilarity between magnitude functions of finite metric spaces.

1 code implementation • 11 Oct 2023 • Ernst Roell, Bastian Rieck

The Euler Characteristic Transform (ECT) has proven to be a powerful representation, combining geometrical and topological characteristics of shapes and graphs.

1 code implementation • 7 Sep 2023 • Franz Srambical, Bastian Rieck

Existing approaches for classifying dynamic graphs either lift graph kernels to the temporal domain, or use graph neural networks (GNNs).

no code implementations • 26 Jul 2023 • Salome Kazeminia, Carsten Marr, Bastian Rieck

In biomedical data analysis, Multiple Instance Learning (MIL) models have emerged as a powerful tool to classify patients' microscopy samples.

no code implementations • 1 Jun 2023 • Bastian Rieck, Corinna Coupette

With machine learning conferences growing ever larger, and reviewing processes becoming increasingly elaborate, more data-driven insights into their workings are required.

1 code implementation • 30 May 2023 • Leon Hetzel, Johanna Sommer, Bastian Rieck, Fabian Theis, Stephan Günnemann

Recent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions.

no code implementations • 9 May 2023 • Rayna Andreeva, Katharina Limbeck, Bastian Rieck, Rik Sarkar

Deep learning models have seen significant successes in numerous applications, but their inner workings remain elusive.

no code implementations • 8 Mar 2023 • Kalyan Varma Nadimpalli, Amit Chattopadhyay, Bastian Rieck

We also show a favourable property, namely injectivity and discuss the stability of the topological loss function based on the Euler Characteristic Transform.

no code implementations • 20 Feb 2023 • Bastian Rieck

Persistent homology, a technique from computational topology, has recently shown strong empirical performance in the context of graph classification.

1 code implementation • NeurIPS 2023 • Joshua Southern, Jeremy Wayland, Michael Bronstein, Bastian Rieck

Graph generative model evaluation necessitates understanding differences between graphs on the distributional level.

1 code implementation • 21 Oct 2022 • Corinna Coupette, Sebastian Dalleiger, Bastian Rieck

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

1 code implementation • 30 Sep 2022 • Julius von Rohrscheidt, Bastian Rieck

The manifold hypothesis, which assumes that data lies on or close to an unknown manifold of low intrinsic dimension, is a staple of modern machine learning research.

1 code implementation • 30 Aug 2022 • Dominik J. E. Waibel, Ernst Röell, Bastian Rieck, Raja Giryes, Carsten Marr

Diffusion models are a special type of generative model, capable of synthesising new data from a learnt distribution.

1 code implementation • 16 Jun 2022 • Celia Hacker, Bastian Rieck

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

2 code implementations • 16 Jun 2022 • Corinna Coupette, Jilles Vreeken, Bastian Rieck

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

1 code implementation • 15 Jun 2022 • Renming Liu, Semih Cantürk, Frederik Wenkel, Sarah McGuire, Xinyi Wang, Anna Little, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek

Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry.

no code implementations • 8 Jun 2022 • Dhananjay Bhaskar, Kincaid MacDonald, Oluwadamilola Fasina, Dawson Thomas, Bastian Rieck, Ian Adelstein, Smita Krishnaswamy

We introduce a new intrinsic measure of local curvature on point-cloud data called diffusion curvature.

no code implementations • 28 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.

1 code implementation • 3 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.

1 code implementation • 16 Feb 2022 • Florian Graf, Sebastian Zeng, Bastian Rieck, Marc Niethammer, Roland Kwitt

We study the excess capacity of deep networks in the context of supervised classification.

no code implementations • 18 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, have emerged as a powerful tool for machine learning with graphs and relational data.

no code implementations • 15 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.

1 code implementation • 28 Oct 2021 • Michael F. Adamer, Edward De Brouwer, Leslie O'Bray, Bastian Rieck

Furthermore, we demonstrate practical use cases of magnitude for machine learning applications and propose a novel magnitude model that consists of a computationally efficient magnitude computation and a learnable metric.

no code implementations • 27 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.

no code implementations • 12 Jul 2021 • Michael Moor, Nicolas Bennet, Drago Plecko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten Borgwardt

Here, we developed and validated a machine learning (ML) system for the prediction of sepsis in the ICU.

2 code implementations • ICLR 2022 • Leslie O'Bray, Max Horn, Bastian Rieck, Karsten Borgwardt

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

no code implementations • ICLR Workshop GTRL 2021 • Feng Gao, Jessica Moore, Bastian Rieck, Valentina Greco, Smita Krishnaswamy

However the function of calcium signaling in epithelial cells is not well understood.

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.

no code implementations • 31 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.

no code implementations • 22 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.

no code implementations • 12 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.

1 code implementation • 7 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.

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.

1 code implementation • 13 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.

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.

2 code implementations • 25 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.

2 code implementations • 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.

no code implementations • 31 Jul 2019 • Bastian Rieck, Filip Sadlo, Heike Leitte

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

no code implementations • 31 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.

1 code implementation • Proceedings of the 36th International Conference on Machine Learning 2019 • Bastian Rieck, Christian Bock, Karsten Borgwardt

The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks.

Ranked #1 on Graph Classification on MUTAG (Mean Accuracy metric)

2 code implementations • 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.

Ranked #7 on Graph Classification on NCI1

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.

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.

no code implementations • 16 Apr 2019 • Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean Bodenham, Karsten Borgwardt, Gunnar Rätsch, Tobias M. Merz

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems.

2 code implementations • 5 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.

2 code implementations • ICLR 2019 • Bastian Rieck, Matteo Togninalli, Christian Bock, Michael Moor, Max Horn, Thomas Gumbsch, Karsten Borgwardt

While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data.

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