End-to-end signal classification in signed cumulative distribution transform space
This paper presents a new end-to-end signal classification method using the signed
cumulative distribution transform (SCDT). We adopt a transport generative model to define …
cumulative distribution transform (SCDT). We adopt a transport generative model to define …
Data representation with optimal transport
arXiv:2406.15503v1 [math.OC] 19 Jun 2024 Page 1 Data representation with optimal
transport Rocıo Dıaz Martın[0000−0002−3732−6296] and Ivan Vladimir Medri[0000−0003−2419−2193] …
transport Rocıo Dıaz Martın[0000−0002−3732−6296] and Ivan Vladimir Medri[0000−0003−2419−2193] …
Data-driven identification of parametric governing equations of dynamical systems using the signed cumulative distribution transform
AHM Rubaiyat, DH Thai, JM Nichols… - Computer Methods in …, 2024 - Elsevier
This paper presents a novel data-driven approach to identify partial differential equation
(PDE) parameters of a dynamical system. Specifically, we adopt a mathematical “transport” …
(PDE) parameters of a dynamical system. Specifically, we adopt a mathematical “transport” …
Transport-based morphometry of nuclear structures of digital pathology images in cancers
MSE Rabbi, N Ironside, JA Ozolek, R Singh… - arXiv preprint arXiv …, 2023 - arxiv.org
Alterations in nuclear morphology are useful adjuncts and even diagnostic tools used by
pathologists in the diagnosis and grading of many tumors, particularly malignant tumors …
pathologists in the diagnosis and grading of many tumors, particularly malignant tumors …
System Identification Using the Signed Cumulative Distribution Transform In Structural Health Monitoring Applications
AHM Rubaiyat, DH Thai, JM Nichols… - arXiv preprint arXiv …, 2023 - arxiv.org
This paper presents a novel, data-driven approach to identifying partial differential equation
(PDE) parameters of a dynamical system in structural health monitoring applications …
(PDE) parameters of a dynamical system in structural health monitoring applications …
[HTML][HTML] Linear optimal transport subspaces for point set classification
Learning from point sets is an essential component in many computer vision and machine
learning applications. Native, unordered, and permutation invariant set structure space is …
learning applications. Native, unordered, and permutation invariant set structure space is …
Invariance encoding in sliced-Wasserstein space for image classification with limited training data
Deep convolutional neural networks (CNNs) are broadly considered to be state-of-the-art
generic end-to-end image classification systems. However, they are known to underperform …
generic end-to-end image classification systems. However, they are known to underperform …
Geodesic Properties of a Generalized Wasserstein Embedding for Time Series Aanalysis
Transport-based metrics and related embeddings (transforms) have recently been used to
model signal classes where nonlinear structures or variations are present. In this paper, we …
model signal classes where nonlinear structures or variations are present. In this paper, we …
Linear optimal transport subspaces for point set classification
Learning from point sets is an essential component in many computer vision and machine
learning applications. Native, unordered, and permutation invariant set structure space is …
learning applications. Native, unordered, and permutation invariant set structure space is …