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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...
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07/06/2022
Unsupervised Manifold Alignment with Joint Multidimensional Scaling
We introduce Joint Multidimensional Scaling, a novel approach for unsupe...
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06/02/2022
Approximate Network Motif Mining Via Graph Learning
Frequent and structurally related subgraphs, also known as network motif...
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02/07/2022
Structure-Aware Transformer for Graph Representation Learning
The Transformer architecture has gained growing attention in graph repre...
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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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10/28/2021
The magnitude vector of images
The magnitude of a finite metric space is a recently-introduced invarian...
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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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02/15/2021
Topological Graph Neural Networks
Graph neural networks (GNNs) are a powerful architecture for tackling gr...
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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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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
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
While many approaches to make neural networks more fathomable have been ...
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08/24/2015
Searching for significant patterns in stratified data
Significant pattern mining, the problem of finding itemsets that are sig...
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03/29/2013