Results 191 to 200 of about 2,509,053 (235)

Experimentally achieving minimal dissipation via thermodynamically optimal transport. [PDF]

Nat Commun
Oikawa S, Nakayama Y, Ito S, Sagawa T, Toyabe S.   +4 more
europepmc   +1 more source

The Geometry of Financial Institutions -Wasserstein Clustering of Financial Data. [PDF]

Math Financ Econ
Riess L, Backhoff J, Beiglböck M, Temme J, Wolf A.   +4 more
europepmc   +1 more source

MM-WAE: Multimodal Wasserstein Autoencoders for Semi-Supervised Wafer Map Defect Recognition. [PDF]

Micromachines (Basel)
Zhang Y, Sun Q, Liu Z, Zhang DW.
europepmc   +1 more source

More Sophisticated Is Not Always Better: A Comparison of Similarity Measures for Unsupervised Learning of Pathways in Biomolecular Simulations. [PDF]

J Phys Chem B
Jäger M, Wolf S.
europepmc   +1 more source

Research and Application of Safety Hazard Perception and Responsibility Traceability System in University Laboratories. [PDF]

Sensors (Basel)
Liu R, Ding Y, Zhu X, Xia X.
europepmc   +1 more source

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An Intelligent Method for Early Motor Bearing Fault Diagnosis Based on Wasserstein Distance Generative Adversarial Networks Meta Learning

IEEE Transactions on Instrumentation and Measurement, 2023
The fault diagnosis method based on generative adversarial networks (GANs) has been successfully applied to the early fault detection of motor bearings, and it has effectively solved the problems of small samples, unlabeled sample features, and data ...
Pei'en Luo, Zhonggang Yin, D. Yuan, F. Gao, Jing Liu   +4 more
semanticscholar   +1 more source

Unsupervised domain adaptation of bearing fault diagnosis based on Join Sliced Wasserstein Distance.

ISA transactions, 2022
Deep neural networks have been successfully utilized in the mechanical fault diagnosis, however, a large number of them have been based on the same assumption that training and test datasets followed the same distributions.
Pengfei Chen, Rongzhen Zhao, Tianjing He, Kongyuan Wei, Qidong Yang   +4 more
semanticscholar   +1 more source

Wasserstein Distance-Based Full-Waveform Inversion With a Regularizer Powered by Learned Gradient

IEEE Transactions on Geoscience and Remote Sensing, 2023
Full-waveform inversion (FWI) is a powerful technique for building high-quality subsurface geological structures. It is known to suffer from local minima problems when a good starting model is lost.
Fangshu Yang, Jianwei Ma
semanticscholar   +1 more source

Fault Diagnosis of Rotating Machinery Based on Wasserstein Distance and Feature Selection

IEEE Transactions on Automation Science and Engineering, 2022
This article presents a fault diagnosis algorithm for rotating machinery based on the Wasserstein distance. Recently, the Wasserstein distance has been proposed as a new research direction to find better distribution mapping when compared with other ...
Francesco Ferracuti, A. Freddi, A. Monteriù, L. Romeo   +3 more
semanticscholar   +1 more source

Convergence of Deterministic and Stochastic Diffusion-Model Samplers: A Simple Analysis in Wasserstein Distance

arXiv.org
We provide new convergence guarantees in Wasserstein distance for diffusion-based generative models, covering both stochastic (DDPM-like) and deterministic (DDIM-like) sampling methods.
Eliot Beyler, Francis Bach
semanticscholar   +1 more source