Computational optimal transport: With applications to data science
Optimal transport (OT) theory can be informally described using the words of the French
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …
Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice
E Zio - Reliability Engineering & System Safety, 2022 - Elsevier
We are performing the digital transition of industry, living the 4th industrial revolution,
building a new World in which the digital, physical and human dimensions are interrelated in …
building a new World in which the digital, physical and human dimensions are interrelated in …
Optimal mass transport: Signal processing and machine-learning applications
Transport-based techniques for signal and data analysis have recently received increased
interest. Given their ability to provide accurate generative models for signal intensities and …
interest. Given their ability to provide accurate generative models for signal intensities and …
Hierarchical gaussian descriptor for person re-identification
Describing the color and textural information of a person image is one of the most crucial
aspects of person re-identification. In this paper, we present a novel descriptor based on a …
aspects of person re-identification. In this paper, we present a novel descriptor based on a …
Deep learning approaches for similarity computation: A survey
The requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …
common but vital task in various domains, such as data mining, machine learning and so on …
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
We prove several fundamental statistical bounds for entropic OT with the squared Euclidean
cost between subgaussian probability measures in arbitrary dimension. First, through a new …
cost between subgaussian probability measures in arbitrary dimension. First, through a new …
Softpoolnet: Shape descriptor for point cloud completion and classification
Point clouds are often the default choice for many applications as they exhibit more flexibility
and efficiency than volumetric data. Nevertheless, their unorganized nature–points are …
and efficiency than volumetric data. Nevertheless, their unorganized nature–points are …
Sliced wasserstein distance for learning gaussian mixture models
Gaussian mixture models (GMM) are powerful parametric tools with many applications in
machine learning and computer vision. Expectation maximization (EM) is the most popular …
machine learning and computer vision. Expectation maximization (EM) is the most popular …
Combining multiple kernel methods on riemannian manifold for emotion recognition in the wild
In this paper, we present the method for our submission to the Emotion Recognition in the
Wild Challenge (EmotiW 2014). The challenge is to automatically classify the emotions …
Wild Challenge (EmotiW 2014). The challenge is to automatically classify the emotions …
Discriminant analysis on Riemannian manifold of Gaussian distributions for face recognition with image sets
This paper presents a method named Discriminant Analysis on Riemannian manifold of
Gaussian distributions (DARG) to solve the problem of face recognition with image sets. Our …
Gaussian distributions (DARG) to solve the problem of face recognition with image sets. Our …