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 …
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 …
Sliced Wasserstein kernels for probability distributions
Optimal transport distances, otherwise known as Wasserstein distances, have recently
drawn ample attention in computer vision and machine learning as powerful discrepancy …
drawn ample attention in computer vision and machine learning as powerful discrepancy …
Linear optimal transport embedding: provable Wasserstein classification for certain rigid transformations and perturbations
C Moosmüller, A Cloninger - … and Inference: A Journal of the …, 2023 - academic.oup.com
Discriminating between distributions is an important problem in a number of scientific fields.
This motivated the introduction of Linear Optimal Transportation (LOT), which embeds the …
This motivated the introduction of Linear Optimal Transportation (LOT), which embeds the …
Pooling by sliced-Wasserstein embedding
N Naderializadeh, JF Comer… - Advances in …, 2021 - proceedings.neurips.cc
Learning representations from sets has become increasingly important with many
applications in point cloud processing, graph learning, image/video recognition, and object …
applications in point cloud processing, graph learning, image/video recognition, and object …
The radon cumulative distribution transform and its application to image classification
Invertible image representation methods (transforms) are routinely employed as low-level
image processing operations based on which feature extraction and recognition algorithms …
image processing operations based on which feature extraction and recognition algorithms …
De-multiplexing vortex modes in optical communications using transport-based pattern recognition
Free space optical communications utilizing orbital angular momentum beams have recently
emerged as a new technique for communications with potential for increased channel …
emerged as a new technique for communications with potential for increased channel …
Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning
Many diseases have no visual cues in the early stages, eluding image-based detection.
Today, osteoarthritis (OA) is detected after bone damage has occurred, at an irreversible …
Today, osteoarthritis (OA) is detected after bone damage has occurred, at an irreversible …
A Transportation Distance for Signal Analysis
Transport-based distances, such as the Wasserstein distance and earth mover's distance,
have been shown to be an effective tool in signal and image analysis. The success of …
have been shown to be an effective tool in signal and image analysis. The success of …