Sliced optimal transport reduces optimal transport on multi-dimensional domains to transport
on the line. More precisely, sliced optimal transport is the concatenation of the well-known …

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 …

We present a new method for face recognition from digital images acquired under varying
illumination conditions. The method is based on mathematical modeling of local gradient …

This paper presents a new mathematical signal transform that is especially suitable for
decoding information related to non-rigid signal displacements. We provide a measure …

Learning from set-structured data is an essential problem with many applications in machine
learning and computer vision. This paper focuses on non-parametric and data-independent …

We sought to develop new quantitative approaches to characterize the spatial distribution of
mammographic density and contrast enhancement of suspicious contrast-enhanced …

This paper presents a new method to classify 1D signals using the signed cumulative
distribution transform (SCDT). The proposed method exploits certain linearization properties …

Intraoperative identification of head and neck cancer tissue is essential to achieve complete
tumor resection and mitigate tumor recurrence. Mesoscopic fluorescence lifetime imaging …

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 …

Problems with dominant advection, discontinuities, travelling features, or shape variations
are widespread in computational mechanics. However, classical linear model reduction and …