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Topological Data Analysis of copy number alterations in cancer
Identifying subgroups and properties of cancer biopsy samples is a cruci...
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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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Graph Kernels: State-of-the-Art and Future Challenges
Graph-structured data are an integral part of many application domains, ...
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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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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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Path Imputation Strategies for Signature Models
The signature transform is a 'universal nonlinearity' on the space of co...
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Set Functions for Time Series
Despite the eminent successes of deep neural networks, many architecture...
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Topological Machine Learning with Persistence Indicator Functions
Techniques from computational topology, in particular persistent homolog...
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Persistent Intersection Homology for the Analysis of Discrete Data
Topological data analysis is becoming increasingly relevant to support t...
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Wasserstein Weisfeiler-Lehman Graph Kernels
Graph kernels are an instance of the class of R-Convolution kernels, whi...
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Topological Autoencoders
We propose a novel approach for preserving topological structures of the...
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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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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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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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