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09/07/2023
Filtration Surfaces for Dynamic Graph Classification
Existing approaches for classifying dynamic graphs either lift graph ker...
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07/26/2023
Topologically-Regularized Multiple Instance Learning for Red Blood Cell Disease Classification
Diagnosing rare anemia disorders using microscopic images is challenging...
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06/01/2023
Evaluating the "Learning on Graphs" Conference Experience
With machine learning conferences growing ever larger, and reviewing pro...
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05/30/2023
MAGNet: Motif-Agnostic Generation of Molecules from Shapes
Recent advances in machine learning for molecules exhibit great potentia...
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05/09/2023
Metric Space Magnitude and Generalisation in Neural Networks
Deep learning models have seen significant successes in numerous applica...
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04/24/2023
DONUT – Creation, Development, and Opportunities of a Database
DONUT is a database of papers about practical, real-world uses of Topolo...
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03/08/2023
Euler Characteristic Transform Based Topological Loss for Reconstructing 3D Images from Single 2D Slices
The computer vision task of reconstructing 3D images, i.e., shapes, from...
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02/20/2023
On the Expressivity of Persistent Homology in Graph Learning
Persistent homology, a technique from computational topology, has recent...
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01/30/2023
Curvature Filtrations for Graph Generative Model Evaluation
Graph generative model evaluation necessitates understanding differences...
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10/21/2022
Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework
Bridging geometry and topology, curvature is a powerful and expressive i...
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09/30/2022
TOAST: Topological Algorithm for Singularity Tracking
The manifold hypothesis, which assumes that data lie on or close to an u...
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08/30/2022
A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images
Diffusion models are a class of generative models, showing superior perf...
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06/16/2022
On the Surprising Behaviour of node2vec
Graph embedding techniques are a staple of modern graph learning researc...
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06/16/2022
All the World's a (Hyper)Graph: A Data Drama
We introduce Hyperbard, a dataset of diverse relational data representat...
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06/15/2022
Taxonomy of Benchmarks in Graph Representation Learning
Graph Neural Networks (GNNs) extend the success of neural networks to gr...
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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...
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03/28/2022
Time-inhomogeneous diffusion geometry and topology
Diffusion condensation is a dynamic process that yields a sequence of mu...
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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...
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12/18/2021
Weisfeiler and Leman go Machine Learning: The Story so far
In recent years, algorithms and neural architectures based on the Weisfe...
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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...
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10/28/2021
The magnitude vector of images
The magnitude of a finite metric space is a recently-introduced invarian...
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10/27/2021
Towards a Taxonomy of Graph Learning Datasets
Graph neural networks (GNNs) have attracted much attention due to their ...
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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...
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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. ...
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04/25/2021
Basic Analysis of Bin-Packing Heuristics
The bin-packing problem continues to remain relevant in numerous applica...
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02/15/2021
Topological Graph Neural Networks
Graph neural networks (GNNs) are a powerful architecture for tackling gr...
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11/22/2020
Topological Data Analysis of copy number alterations in cancer
Identifying subgroups and properties of cancer biopsy samples is a cruci...
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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...
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11/07/2020
Graph Kernels: State-of-the-Art and Future Challenges
Graph-structured data are an integral part of many application domains, ...
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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...
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06/14/2020
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
Functional magnetic resonance imaging (fMRI) is a crucial technology for...
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05/25/2020
Path Imputation Strategies for Signature Models
The signature transform is a 'universal nonlinearity' on the space of co...
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09/26/2019
Set Functions for Time Series
Despite the eminent successes of deep neural networks, many architecture...
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07/31/2019
Topological Machine Learning with Persistence Indicator Functions
Techniques from computational topology, in particular persistent homolog...
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07/31/2019
Persistent Intersection Homology for the Analysis of Discrete Data
Topological data analysis is becoming increasingly relevant to support t...
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06/04/2019
Wasserstein Weisfeiler-Lehman Graph Kernels
Graph kernels are an instance of the class of R-Convolution kernels, whi...
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06/03/2019
Topological Autoencoders
We propose a novel approach for preserving topological structures of the...
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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...
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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...
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12/23/2018